{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CelebA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we show the results for CelebA. You can create from scratch the features or use the pre-computed ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to create the features, first of all unzip file \"dataset_celebA.7z \". Be sure to save the folder together with this notebook. You also need to download  [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).\n",
    "\n",
    "Otherwise, just jump to section 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA Saved\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import radialProfile\n",
    "import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "data= {}\n",
    "epsilon = 1e-8\n",
    "N = 80\n",
    "y = []\n",
    "error = []\n",
    "\n",
    "number_iter = 1000\n",
    "\n",
    "psd1D_total = np.zeros([number_iter, N])\n",
    "label_total = np.zeros([number_iter])\n",
    "psd1D_org_mean = np.zeros(N)\n",
    "psd1D_org_std = np.zeros(N)\n",
    "\n",
    "\n",
    "cont = 0\n",
    "\n",
    "#fake data\n",
    "rootdir = 'dataset_celebA/'\n",
    "\n",
    "for filename in glob.glob(rootdir+\"*.jpg\"):\n",
    "    img = cv2.imread(filename,0)\n",
    "    \n",
    "    f = np.fft.fft2(img)\n",
    "    fshift = np.fft.fftshift(f)\n",
    "    fshift += epsilon\n",
    "    \n",
    "    magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "    psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "    # Calculate the azimuthally averaged 1D power spectrum\n",
    "    points = np.linspace(0,N,num=psd1D.size) # coordinates of a\n",
    "    xi = np.linspace(0,N,num=N) # coordinates for interpolation\n",
    "\n",
    "    interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "    interpolated /= interpolated[0]\n",
    "\n",
    "    psd1D_total[cont,:] = interpolated             \n",
    "    label_total[cont] = 1\n",
    "    cont+=1\n",
    "\n",
    "    if cont == number_iter:\n",
    "        break\n",
    "\n",
    "for x in range(N):\n",
    "    psd1D_org_mean[x] = np.mean(psd1D_total[:,x])\n",
    "    psd1D_org_std[x]= np.std(psd1D_total[:,x])\n",
    "    \n",
    "\n",
    "## real data\n",
    "psd1D_total2 = np.zeros([number_iter, N])\n",
    "label_total2 = np.zeros([number_iter])\n",
    "psd1D_org_mean2 = np.zeros(N)\n",
    "psd1D_org_std2 = np.zeros(N)\n",
    "\n",
    "cont = 0\n",
    "rootdir2 = '/home/duralllopez/DATASETS/celebA/img_align_celeba/'\n",
    "\n",
    "\n",
    "for filename in glob.glob(rootdir2+\"*.jpg\"):     \n",
    "    img = cv2.imread(filename,0)\n",
    "\n",
    "    f = np.fft.fft2(img)\n",
    "    fshift = np.fft.fftshift(f)\n",
    "    fshift += epsilon\n",
    "\n",
    "    magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "\n",
    "    # Calculate the azimuthally averaged 1D power spectrum\n",
    "    psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "\n",
    "    points = np.linspace(0,N,num=psd1D.size) # coordinates of a\n",
    "    xi = np.linspace(0,N,num=N) # coordinates for interpolation\n",
    "\n",
    "    interpolated = griddata(points,psd1D,xi,method='cubic')\n",
    "\n",
    "    interpolated /= interpolated[0]\n",
    "\n",
    "    psd1D_total2[cont,:] = interpolated             \n",
    "    label_total2[cont] = 0\n",
    "    cont+=1\n",
    "    \n",
    "    if cont == number_iter:\n",
    "        break\n",
    "\n",
    "for x in range(N):\n",
    "    psd1D_org_mean2[x] = np.mean(psd1D_total2[:,x])\n",
    "    psd1D_org_std2[x]= np.std(psd1D_total2[:,x])\n",
    "    \n",
    "\n",
    "y.append(psd1D_org_mean)\n",
    "y.append(psd1D_org_mean2)\n",
    "error.append(psd1D_org_std)\n",
    "error.append(psd1D_org_std2)\n",
    "\n",
    "psd1D_total_final = np.concatenate((psd1D_total,psd1D_total2), axis=0)\n",
    "label_total_final = np.concatenate((label_total,label_total2), axis=0)\n",
    "\n",
    "data[\"data\"] = psd1D_total_final\n",
    "data[\"label\"] = label_total_final\n",
    "\n",
    "output = open('celeba_low_1000.pkl', 'wb')\n",
    "pickle.dump(data, output)\n",
    "output.close()\n",
    "\n",
    "print(\"DATA Saved\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Loading Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load the features. Either the pre-computed ones or the features that you have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# load feature file\n",
    "pkl_file = open('celeba_low_1000.pkl', 'rb')\n",
    "data = pickle.load(pkl_file)\n",
    "pkl_file.close()\n",
    "X = data[\"data\"]\n",
    "y = data[\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the label distribution, to be sure that we have a balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8cb9cf9d30>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Check Spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a look to the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power Spectrum')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ts4iIiIjI8Bhjel2cTicyMzNxyy23YP369bCj+DWoButEqaQkYMwYoKLCibKWDGS21iM+zomScjfSUzt9f8EDDwAbN7La94c/AHfcwTQaqVwuVilTU7uva20Fysq4lYiXMd1DfFJT2SobH999CfVkWhERERGJWU888QQAoL29HadOncIrr7yC999/H0VFRfjRj34U5rMLD4XIKJWY6A2RQFlrNua1XIInLQHll+Mwc3IrEuJ9vDMSHw/cdx/w7rvAuXPAm28CS5cCCxeGbi/JYPMGw56s5dCgK1e4pUhnjxBtDKe/JiYySCclMWTGxQFud/dHp7P7WP1d4uOj52clIiIiIsP25JNP9vp827ZtWLNmDX7yk5/gq1/9KgoKCsJzYmGkEBmlnE6goIADTsvrkmA7u2AMYGBxscqF/PHtvr/Q5QLuugsoLgb27gV272YSvfXW68NZtDDGd7j06ujgpaamO2T2bT8wZuC22K4u/tBnzVKQFBERERmlVq1ahVmzZuHIkSMoLi72GSI3btyI559/Hrt370Z9fT0mTJiAT3ziE/jHf/xHpKen97rvpk2b8Jvf/AYffPABLly4gPb2dkydOhWf/OQn8fjjjyMhIWGkvrWAKURGscmTmZtaOtyobktCVkcHUpMNSi7GYfK4dv85xxigsJDDbN57j62hr7wC3Hkn103GGpdr4D0yrR04GFrLCm5nJzBnjtpkRUREREYp73pIt9t93W1PP/00nnjiCWRmZuL+++9Hbm4uDhw4gO9973t48803sWPHDng8nmv3/+53v4tjx47hxhtvxNq1a9HS0oJt27bhySefxObNm/HOO+/A6fQx9ySMFCKjWEYGM19FhUF5xxhkNX2EeI8HtTUuNDQ5kJrc1f8BJk0CPvEJ4O23ua3Gq68CN98MTJs2Mt9AJAmksmgMB/589BGD5Lx53W2wIiIiIjIqbNmyBcePH0dcXByWLVvW67ZNmzbhiSeewMqVK/Hmm2/2qjquX78en/3sZ/HEE0/gX//1X69d/5Of/AQFBQUwfV6PfvOb38QzzzyDl19+GY888khov6lBUoiMYt3DdYCy5nTMSz4LAHA6unCpxjlwiAQ4ufXBBzn19PhxrpesqABWrFBA8sUYVnDLytgiu2DBwFVOERERkRjRZ3lgxArmeXrXRPYcrGOtxfe+9z2MGzeu131feOEFAMDPf/7z69pWP/OZz+D555/Hr3/9614hcsqUKT4f92//9m/xzDPPYOPGjQqREjze4ToAUFadAJvrgOnqgicZKLkYj4K8flpae3K5WIHMzQW2bwcOHQIuXWJ7a3JySL+HqJWby303P/yQg4l8tDKIiIiISPR76qmnen1ujMG///u/47Of/ex1992xYwfcbjdeeuklvPTSS9fd3tbWhkuXLqGqqgpZV5eRNTY24vnnn8crr7yCEydOoL6+vtf2IaWlpUH+joZPITKKxccDaWmsSDY1OVDtyEZWSx3cSUmoqTOob3TAkxJANRJghW3OHPbH/ulPrEb+/vfA6tVse1VV8nrZ2dzHsqgIWLKEk15FREREYli0VCKDyRvoGhsbsWPHDvz1X/81vvCFL2Dy5Mm47bbbet23qqoKHR0d1wXPvhoaGpCVlYX29nbcdttt2L17N+bNm4dHHnkEOTk519ZaPvXUU2htbQ3NNzYMCpFRzBggM5PL9EpKgPKOXGR1XgKSkuByAhXVLnhS2gZ30DFjgIceYltraSnXS8bFcYpPQQEwcaLaN3vKzOTU1z17GCQjcHqWiIiIiAxfcnIy7rjjDrz++utYvHgxPv3pT+P48eNISkq6dp+0tDR0dXWhuro6oGO+9tpr2L17Nz796U9j/fr1vW4rLy8fMIyGi8ZLRrn09O6BqqVXkq5tU+FJ7sRHFXED7lrhU2Ii95NcsYIHb2sDTp5koPzlL1mpPHWK1wsnHLW0cLuU5uZwn42IiIiIhNANN9yAz33uc7hw4UKvtY0AsGLFCtTU1ODw4cMBHevUqVMAgIceeui6295///3hn2yIKERGOY+nO0SWX3LDJiUDra1wuYDWNoPahiG2oTocHBrz8MPAI48Ay5dzHWBHB3DmDCuV//EfwIYNHMjT2Bi8byoapadzYuvOnfpZiIiIiMS4f/qnf0JCQgK+973voaam5tr1X/nKVwAAn/vc51BWVnbd1zU2NmLnzp3XPs/PzwcAbN68udf9zpw5g8cffzz4Jx4k6kuMcomJQEoKLw0NQFXcWGTXnQXi4+F2saU1PbVzeA+Sns7hMQsXAvX1wNmzvFRUsI+2pIT3S0sDxo3jZfx4ntRo4vHwl7BrF6u4PVobRERERCR25OXl4fOf/zyef/55PPvss/j2t78NALj99tvxne98B9/4xjcwffp03HfffSgoKEBDQwNKSkrw/vvvY/Xq1diwYQMA4IEHHsC0adPwgx/8AAcPHsSiRYtw/vx5vPHGG1i7di3Onz8fzm/TL1Uio1xiItdGjh/Pz8ubMzgptL4eqcmd+OiiG10BztYJSGoqcMMNwMc+BvyP/8HBOxMn8jFra4Fjx4BNm4Bf/xr4r/8CNm/mdbW1GFpvbZRJSWEVd88etriKiIiISEz6xje+gaSkJLzwwguoqKi4dv3jjz+OLVu2YO3atdi2bRuee+45vPTSSygtLcVjjz2GZ5555tp9k5OT8d577+HP//zPcfjwYbzwwgs4cOAAvvnNb+I///M/w/FtBcTY0fDCfpAKCwttUVFRuE8jYO+/D5w7B2zbBuTnA3ff0soW06ZGVHZlY8W8RmR4hlmNHEhXF7e8KC/n5eJFoO8kqdRUYMoUYNo09uAGtP9IlKqt5fjcwkJ+FBEREYkSR48exezZs8N9GhIigf5+jTHF1tpCX7epnTUGZGYCdXX8d3k5YOPiYebMAU6dQlxJNS5eTgp9iHQ4uGYyN5drKbu6uP2FN1SWl7MVdv9+XtLTgalTGSj7bMQaE9LSOLV1714GSe0jKSIiIiIxQiEyBqSnM6NcWxdZBWRnu4AZM5DqKsGF4xWYMcEBZ9wI7vXocHAfxexsYP58trJWVHCq65kzwJUrQHExL1lZDJNTp7JaGSsyMvjL2L+f60m1NYqIiIiIxAC9qo0Bycn8OH48cOIEi37Z2QAcDjin5qOjORG1JQeQme8JX0XMGGDsWF5uvBEoK2OgPHeOQauqigNpxoxhT+6ECbHR8pqVxTbfgwdZoXVoGbKIiIiIRDeFyBiQmMiP3hB5/jyLfwAAYxA/aSzKjBOZNUWcIJqQELZzBcAgNWECLx0dwIULDJQlJaxWVlQwUCYkAHl53feN1mmv2dlcI+pyAfPmRX8wFhEREZFRTSEyBiQkMJdNnMiPZWUcDOrNiikpQNmVHMxeugLOD4sY3CIlkLlcrDzm5wPt7UzAH30ElJayN/f0aV4ArjP0Bspx46JrYE1ODsOyywXMmqUgKSIiIiJRSyEyBhjDAmNnJ7NVaSmLejNn8nank7fVIAPZK1cCRUVckxhpA23cbq6LnDqVayhraxm8SkuZjGtreTl8uPubzszsffF4IrNl1BgGyTNnGCSnTw/3GYmIiIiIDIlCZIzIzGQBr6CAmevs2e4QCbAqWVYGZN+QAqxYAXz4IdfqZWeH76T7YwxDbno6W0C7uoDKSn5zpaX8tzdUnj3b/XVOJwfaZGV1h8qEBFYtvRfnCA4Y6vs95eay59jtZvVVRERERCTKKETGiLQ0FrkKCrhf5IULQFsbEBfH21NSOHBnzhzAlZAALF0KHDrEZJmTE5nVu54cju7BPEuWsLR65Qq30aiq4nYi1dVsgb18mRd/3O7uQOkNmACP2dXFS2cnq6He6zo7ufh09WoG1OF8Hzk5wJEjDLMTJw79WCIiIiIhYq2F0fKbmGOtDcpxFCJjhHe4TlISc1Z5OZcXTpvG6x0OZqGaGmYYuFzADTfwC06eZEUymragcDoZ5rzbg3i1tvYOlo2NXCDa2tp9aW/npaFhcI9ZWwu8/jqwdu3VH+Iwz/3gQaCpia2tkR7iRUREZNRwOp1ob29HnLcaITGjvb0dziB05UVRapD+eEMkwGpkeTkrkz3zVWIiK5TX8o/DAcyYwSB54ABbR6NpWI0v8fHdFUtfrGWA9AZKb8AEGO4cDl56/tt72bWLi03feAO4917/jxEIl4u/iNOnGSTnzQvf9isiIiIiPaSmpqKurg7ZkbrsSYasrq4OqUHYl10hMkZ4OzQ7Ohgit2/nGsn29u5skpLC3TN6XgeA004TE4HiYt4YKZNbQ8EY9vjGxQGD/QO6805g0yYGvzffBO65h/uqDJXDwX0xL10Cdu8GFi/u/W6AiIiISBhkZmbi/PnzAACPxwO3263W1ihmrUV7ezvq6upQU1ODSZMmDfuYCpExJDOTywRTUphNKioYJKdM4e3GsBBXU8P5Lr1kZQE33hi5k1sjgdMJ3HYbw9/Jk8BbbwF3380QPhxZWWyV3bEDKCzkMCARERGRMImPj8ekSZNQXV2Nc+fOobOzM9ynJMPkdDqRmpqKSZMmIT4InYcKkTEkI4N72qeksBpZUcGWVm+IBNi5+tFHPkIkwC9cuTLyJ7eGk8MB3HILA+WxY8CGDaxQTp48vOOmpbGtdccOViSHs+ZSREREZJji4+Mxbtw4jBs3LtynIhFI0zxiSHIyK40AQyTA4TodHb3vc+lS9zLA68THsxo2bhxTaFdXSM85KjkcwJo1wNy5nNr6pz/13mZkqJKSWIXcs4drL4M0PUtEREREJJgUImNIz+V0Hg8Lie3tHKbj5W1nr67u50AuFzB/PqeGXrrEO7e3h+Sco5YxwKpVnHDb2Qm88w5w6tTwjxsXx/bWw4dZ6VSIFxEREZEIoxAZQxISWCTzFrC8bax9i2RJSb2DpU/GMESuWgXk5wPNzUBlJQNlW1uwTz06GQOsWAEsWsSw9957wPHjwz+uy8V+43Pn2Fqsn7eIiIiIRBCFyBjicHBZo7dV1dvSWlLCYplXUhKXPLa0BHDQtDSGyVtuYaCcMoUPUFnJvRj99sWOEsYAy5YBS5cyvW/eDBw9Gpzj5uYytO/cCdTXD/+YIiIiIiJBoBAZYzIzu8Nhejo7I1tbgdLS7vsYw8BZVjaIAxvDHtmpU4GbbwZWr+YmlB0dDJSXLwONjaO3/XLxYmD5cv57yxZObw2GzEyG0+3bOTVJRERERCTMNJ01xqSnswvSKz+fBcMzZ4CeW8Kkp3MJ38SJQ9jj3hjusZiayspkQwO3BamoYJi0lik1KYk9tqNlX6GFC/lx1y5WJN1u/gKGKyWFA4+Ki1kVnjaNP18RERERkTBQiIwxPSe0Asx4xcUMlp2d3JkC4LK7ri4WtyZOHOaDpqTwMmECH6S+nqHy4sXuUOly8eSCsC9NRFu4kGsYP/yQw3buvRfIyxv+cd1utreePg3U1XHwUaz/LEVEREQkIqmcEWNSUlj88w5Tzchg1bG1FSgv733ftDRWI4O6f6zTyQfMz+fQmdtvZ5tnQQFTq3ctZUALMqPU0qXd239s3MjvORgcDgbJ2lq2t9bWBue4IiIiIiKDoBAZY4xhQdA7h8UY/1Na4+KY5S5dCuEJud1c1zdtGtdRrlkDzJzJ6qR3LWWsDefxbv8xbRrT/JtvDrCnyiClp7Oyu21b78WuIiIiIiIjQCEyBuXkcN6Nl3dK69mz18+98XiAEydGcF/75GRg8mQGyptuYqD0Duepqoqd7SyM4UTbyZMZkt98k22owZKUxKlJ+/YBR44EuZwsIiIiIuKfQmQM8nhYAPQGyawsXtfczNk3PSUkcKhqVdXInydSUtj2umYNA+W0aQyRsVKhdDqBO+4Axo/nD/mPf+THYHG5gDFjgPPngd27+QsWEREREQkxhcgY5HAwtzQ08HNjuquRZ85cf//kZK6NDKuUFPbdrlnDKmXfCmW0rqF0uYC772Z5uK6OFclgfi/G8NjNzWxvrakJ3rFFRERERHxQiIxRY8Z0D9cBeq+L7Nu6mpzM7HHlysidn1/e7UO8FUpfayijreIWFwfcdx+nHFVXAxs29P7lBENaGpCYCOzYAZSUjGB/soiIiIiMNgqRMSotjXnMuwYyJ4fFvsbG67DlLXoAACAASURBVFtaAeaP06dH9hwD0nMN5c03A3PmsE20spITga5cYWUv0kNTQgKDZGoqfwEbN/ZeuBqsx8jKAg4fBg4dCv7xRURERESgEBmzXC4Gx6Ymft6zpbXvlFaAAbOysnuqa0RKSuKmlitXcmjNkiXdm1xWVTFUXr7MbyISA1RKCrB2Lb+P0lJWJIPdputycRuQ8nJg167uJ4CIiIiISJAYGyEVHGPMBABPA7gHQBaAcgCvAnjKWhvQQi9jjAHwWQCPAZgLwAngOID/B+DH1tqARlgWFhbaoqKiQX8Pkaaignve5+Tw84sXgddeYzHsz/6MwbKnK1eYP+bPH/lzHbaODpZZGxoYKL2DebzfpDHd1UpjeHE4uv/ds2xrLS89P/dKTWXFbziqqrg2sqmJ23Xccw9Lx8FWV8efy+LFrFCKiIiIiATIGFNsrS30dZtrpE/GF2PMVADbAeQCeA3AMQDLAHwZwD3GmFXW2kDmh/4SwF8CqATwIoBGAHcAeB7AGmPMJ22kpOYRkJ7enYeM4TrJ5GQW6i5f7g6XXmlpwIULwNSpLJZFFZeL30BaGpCXx+taWhjUurq6L9ZyOwzvpaOj+3pj2CrrcFz/0Rje/+BBHns4QTIrC3jwQba0VlUBr77K4Ttjxw7/59CTx8Ofwc6dwNy5bAvu+86BiIiIiMggRUSIBPATMEB+yVr7Q++VxpgfAPgKgP8D4Av9HcAY8yAYIM8CWGatvXz1ejeA3wF4CMCnAawPwflHpPh4IDOTc2iSkpgf8vO5ZO7MmetDpDHMYh99xFk2US8hYfhVw76Sktgm6j3+UKWmAuvWAe+8wx/4G2+wRXfatKCc5jUJCfxFHzkC1NZyTanbHdzHEBEREZFRJexrIo0xUwDcBeAcgB/3ufkJsJr4l8aY5AEO9YmrH7/vDZAAYK1tB/DNq59+cdgnHGUmTOi9NWHPdZG+arJpacC5c9G/RWPIZGQAy5eznDvc9YxxcWxlnTuXVc533wWKi4M/JMjpZBm6ooJVyWDuVSkiIiIio07YQySA265+fNta29XzBmttPYBtAJIArBjgON5eQB87IV67brExJn2oJxqNMjJ6Z5Jx4ziJtbaWLa19OZ38WFo6MucXlTIygBUruP5yuNuNOBzAqlXAjTeyFFxUBGzaFJrBQFlZbN394ANOURIRERERGYJICJHexskTfm4/efXjjAGO441EBT5um9Lj37N8fbEx5jFjTJExpujSpUsDPFT0SEpi56S3aOZwdHdMHjjg+2vS07ndRyQOOI0Y6ekMko2Nww+SxnCa0V13sdX05Engj38M/uRWgBNiPR5gzx4+TlfXwF8jIiIiItJDJIRI71jKWj+3e68fqIL4xtWPf2eMyfReaYxxAXiqx/0yfH2xtfZn1tpCa21hTt/FglFuwgQWzbxuuIFh8vRpViT7crnYXXnx4sidY1RKS2OQbGoafpAEuGB13TpOP7p4kQN3rlwZ/nH7iovjGN6TJzm+t60t+I8hIiIiIjErEkLkQLzjJAdaKPZbAG8BmArgiDHmZ8aY5wDsA3AfuiuaAW3zEUu8XYxeKSnA9Olsc92/3/fXpKUxY3SOup/WIHmDZHNzcPZkzM4GPv5xfqytZZC8cGH4x+3L4eA6yZoaYPt2bgciIiIiIhKASAiR3lqYv43yPH3u59PV9ZTrAHwNwEVwUutfAbgAYDUA7xYho24xWEoKh3S2t3dft3AhuyhPnPA9ZyUujt2UMdTZGzoeD4fteLcUGa7kZFYk8/M54ejNN9l7HIrdaTIyGCi3bQPKyoJ/fBERERGJOZEQIo9f/ehvzeP0qx/9rZm8xlrbYa39vrV2obU20VrrsdbeA+AIgIUAmgEcHvYZRxljuHViz5bW9HRmlJ5bH/bl8TBkatlcAHoGyWBMP3W7uUZyyRKGxx07gPffD81C1eRkhskPPwSOHlX5WURERET6FQkhctPVj3cZY3qdjzEmFcAqMPztHMZj/CWABAC/u7rlx6iTk9O7EgkAixbx45Ejvme4JCQwD5WXh/78YoLHw9bW1tbgVCSNAQoLgTvv5ELV48eB118PzRYdbjfbW0tKOCFW24CIiIiIiB9hD5HW2tMA3gaQD+B/9bn5KQDJAP7DWtsIAMYYtzFmljFmat9jGWM8Pq5bCuA7ABoAPB3cs48eaWndA3O8cnJYoWxvBw77qc9mZLA41TeAih+pqaxINjUFb7rqlCnAxz7GY1dWAq+8wj0fg80YPimamoAtWzSiV0RERER8CnuIvOpvwLWKLxhjXjXGfNsY8x6Ar4BtrP/Y4755AI4CeNfHcf5kjNlsjPnR1WP8AcAOAPEAHrbW+tpDclRwOLhHZM+WVqC7GnnokO+g6HYzeJ47F/JTjB0eD7BsGYfVBGvyqXfgzvjxrBK+/jork6Hg8XAa08mT3FOyqmrgrxERERGRUSMiQuTVamQhgPUAlgP4Kjhl9QUAK621gb6KfRlAKoC/APB3AOYD+L8A5lprNwb5tKPO2LHstOxp/Hh2Mba0AMeO+f66jAwWpYLRoTlqZGSwFbWmJnhl3MRE4L77gLlzmew3b+Zk1VAsWnU6WZV0u4Fdu4B9+4KzjYmIiIiIRD1jQzHxMcoVFhbaoqKicJ9G0HV0AO++yyKTMd3XnzsHbNzI+Sp/9mfMD33V1HBrwRtuGLHTjQ0XLwLFxawkulzBO+6xY6wSdnayJ/mOO7iINRSs5XYjHR3ArFnAxIksbYuIiIhIzDLGFFtrC33dpleCo4jLxeJS35kpkyezcNbYyA5GX9LTuV3hlSuhP8+YMnYsk/fly8GdejprFnD//UBSElBaynWSly8H7/g9GcMnQHo6pzBt364ngoiIiMgophA5yuTlXd+Wagz3jQTYteirO9IYViqPHg3NdoUxbeJEYPZshrxgtp6OHct1kjk5XH/52mvsOw4Vl4vl6K4uBskjR4K35lNEREREooZC5CiTnu77+qlTOfyzttb/EJ2UFBagKitDdnqxa8oUYNo04NKl4KbwlBRg3Tpgxgy2m77zDrBzZ2g390xOZpi8cAHYulVPCBEREZFRRiFylImPZ+tq3xkpTmf3escPP/Sfc9LSWIDSzg9DMH06kJ8f/NDlcgG33AKsWsW1ivv3A2+9FbwtRnwxhotrExO5r+S+faF9PBERERGJGAqRo9CECb73kp81i5ng8mUus/MlPp4TXi9cCO05xiRj+EPOy2NFMtjHnjcPWLuWv8QLF7hOMtTbc8THc7xvVRWrkqWl6ncWERERiXEKkaNQZqbvbkeXq3c10p+MDODECRWehsThYNjLyQnNIJzx44FPfKJ7neSrr4Z2naRXejr7offvZ2XS17sUIiIiIhITFCJHoaQkLmvru2ckwPkvcXFAWRl3p/DF5WLh68yZ0J5nzHI6gQULOBinsjL46xd9rZPctSu06yQB7ik5ZgxQX8+qZElJ6B9TREREREacQuQoNWUKh+j0FR/PvewBLnPzJyODGaG+PjTnF/O8Zd+pU9naGuxFpn3XSe7bF/p1kl5paXyCHD7MIT++nmgiIiIiErUUIkepsWNZOGpvv/62+fNZLCspAaqrfX+9Mdzb/tgxLYEbMmNYLVywgD/oYAe8nuskExJGbp0kwBA7ZgyfYNu2cRqTr9K3iIiIiEQdhchRyuXisFBfe8YnJnL+C9D/2kiPh0W0kcgkMS0vD1i+nBt4NjQE//jjxwMPPdR7neSpU8F/HF9SUrq3A9myBfjoI6Czc2QeW0RERERCQiFyFBs/np2OvjopFyxgNfL06f4HiXo8LDIpFwxTZiawciX/7SvZD5d3neTMmfyFv/tu6PeT9PJuB+LxAIcOsTLpr8QtIiIiIhFPIXIUc7uBadN8Z5bUVHZCWgts3+6/ZTUxkcWz8vLQnuuokJICrFjBH/6lS8HvE3a5gJtv7r2f5JtvjtyYXZeLVUmHA9ixg2XupqaReWwRERERCRqFyFEuL4+FIl+VxEWLGBIvXux/l4jMTK6N9LW+UgYpPh5YsoS/mFBMbvWuk7z/fo7pLS0F/vu/Q7PdiD+JiVyUW13NFtfTp4M/WEhEREREQkYhcpSLjwcKCoCaGt+3LVvGf+/c6T8kut0MoSUloTvPUcXpZNCbOZMVyVCk83HjgI9/nMNvGhq4TvLEieA/Tn/S0/kOxKlTDJOlpeqLFhEREYkCCpGCiRPZOemr6DVjBpCdzb3j9+/3f4yMDGaB5ubQneeoYgy3/1i8mBW7UPxgU1JYkZw9m+Ft0yauVxzJIOd08gmWmAgcOAB88AFL39pfUkRERCRiKUQKEhKA/HzfayMdDi6hA7jVoL99IZ1OLnk7cyZkpzk6jR0L3Hgj1y3W1QX/+C4XsGYNL04nB9+88UZohvv0Jy6O6yVdLq6V3L49NOtCRURERGTYFCIFADBpEpel+SoAjR3LolhnJ7Brl/9jpKcD58/7D5oyROnpTPJxcaHbT2X2bOCBB4DkZFYCX34Z2Lt35NtLExIYJgGgqIgDeKqrFSZFREREIohCpADgjJVJk4DaWt+3r1jBItHp00BZme/7GMMMcPx46M5z1EpM5ALVnBygoiI07Z5jxgAPP8y1mJ2dwJ49HLpz8WLwH2sgiYkMkx0dfOdi9+6Rr46KiIiIiE8KkXLN5Mmc4eKr6JOSAixcyH9v3+4/w3g8HCoaqoLZqOZ2cwPPadPY6hmKiaYJCcAtt3CtZFoaJy699hqwdSvQ2hr8xxtIcjLDZEsLq5J793IQkIiIiIiEjUKkXJOSAowf778auWAB71NV1X+1MTWVW35oNkoIOBycdrRgAds8Q7XHY14eq5JLlnCt5JEjwO9+x1J0OFpLU1IYJq9c4STX48eBtraRPw8RERERUYiU3goK/L82d7nY1gqwu9BfYSopiTNgKipCc44ChrwVK4CmptAtQnW5gMJC4KGHuDC2qQl45x1g48bwVQPT0tjSW1ICvP8+cOGC3q0QERERGWEKkdKLx8OCj79BoFOmcIvBlhaguNj/cdLSWI3UHvIhlJHBgTsuV2j7hzMygHXrgJtu4uahJSWsSh46FJ6qpMMBZGWx5O3dFkT90yIiIiIjRiFSrjN1qv8uSWO444QxwOHDXDLnS3w8K5UXLoTuPAUs+y5fzupcZWXoqnLGAHPmAJ/8JJ8g7e3cU/LNN7mJaDi43RwG5HBw+E5RkdZLioiIiIwAhUi5Tno6Cz3+Xo9nZwOzZjGv7NjhvxiVkQGcOBGeeSyjSs+BO5WVoV0rmJwM3HEHcNddHMJz4QLw0kvh3SA0MZFhsq6OA4COHdOTTkRERCSEFCLFp2nTuATOn6VLWW386CPuDemLy8WAefZsaM5RenA4gOnTuYaxri701cGCAg7emTiRge1PfwI2bQpveEtL4zsc588DmzcDJ08qTIqIiIiEgEKk+JSRwdfk/rJIYiKweDH/vWOH/z3pMzNZpApXx+OoM2YM+427uvz3GgdLcjJw773A6tV8x+DECe4rWV4e2sftj3e9ZEYG3714/31OlNUkVxEREZGgUYgUn4zhThL9LTGbO5etr7W1nLHii8PBiuXJk6E5T/EhNRVYuZK/nFCukwT4RJk7lxNcc3I4Kfb117lGMZxTlZxOhsm0NODUKYbJc+e4llNEREREhkUhUvzKyup/baTTyawCcA/45mbf90tLA8rKuMWfjJD4eJaKCwoYJEMdntLTgY99rLs8vW8f8Oqr3MsynFwutrh6PNxb8v33OV1WY4NFREREhkwhUvwyBpg9m2sj/RWzJk3ipa0N2LPH/3GSkznvJBw7QoxaDgcnIC1axNbW/ha5BoPTycWy69YxtFVVAb//Pd9hCHdo84bJ1FQ+Ed9/nwt6/fVhi4iIiIhfCpHSL48HyM/vf3ndypXMK8eOAZcv+75PSgqPoSE7YTB+PNdJdnSMTDl47Fi2t86cyZC2Zw/XSpaVhf6xB+INk8nJ7MHesoVrOEPZ8isiIiISYxQiZUBTpzIk+uuITE/nFoLW9r/lR1YWcPQouytlhKWlMe2npgKXLoW+JBwXB9xyC3D//XzsK1e4VnLzZv99zyPJ7QZyc9n2u28f8MEHI/NzEREREYkBCpEyoLg4trX2V41csoSvx8vKOL/EF6eT01o//FB7wodFQgJ/UZMnM8mPRItpXh63Aiks5BPg+HHgxRcjp7c5Pp5h0ukEior4Lki413GKiIiIRDiFSAnIuHEsKPkLfwkJzAkAsHOn/3wSF8f7Fhdr14WwcDr5jsCCBVyzOBJVQZeL4fXhhxkqW1u5JvEPf4icwJaQwDDZ0cEn8J49HDssIiIiItdRiJSAOBzcyaGpyX8Bac4cbs9XV+d/yw+A6yPb24EDB7QULWzy8rhOsq1t5MbmpqcDa9cCt98OJCUBFy9yreSuXZGz9UZyMvfabGwEtm1jq6vK5iIiIiK9KERKwNLS2Anpr3jkcDCXAGxZ7W8YaEYGOypPnAj+eUqA0tP5C0tJGbn1gMYA06YBn/oU35WwlkHtd7/jkyESWlwBrh3NzWW1dutWnmNdXbjPSkRERCQiKETKoEydyhzgr3A0YQKDZn9bfnjl5gKnTwOlpcE/TwmQtw950iSgomLktuKIjwdWr+bektnZrPZt2sQtQSLlCWEMg3ZODt852baN25WozVVERERGOYVIGZT4+IGH7KxYwark8eP+t/wA+Bo9O5ttrf0dT0LM6WQv8oIFDEsjOT11zBjg4x/nJNfkZD5h3ngD2LAhcp4UxrAMn5PDALltG4fwRMr5iYiIiIwwhUgZtPHjuX9kY6Pv29PTgXnzBt7yA+DMFY+Hg3YiYeeHUW3CBG4D0tbGNs6Rai11OLin5KOPAkuXcvuNkhLgpZfYStpfX/RIMoZP1jFjWDndsYPrOaurI6cNV0RERGQEKETKoHmH7DQ0+H/tvGgROyXLyoCzZ/s/XkICi2F7945cN6X4kZ7ONtPx49ne2tIyco/tcgGLFzNMzp3L0HbkCPDb3/JdhkgZvgNwzeSYMfz57NrFQHnpkiZFiYiIyKigEClDkp7OZXT+OvoC3fLDy+NhKD18WEWdsIuLYyl5xQpWJS9fHtlwlJTEIPvww1xg297O9tEXXwROnoysJ0hKSvfWIEVF3LrkwoXICrwiIiIiQaYQKUM2bRpfz/sLiLNnA5mZQH19/1t+eGVnc6bKmTPBPU8ZoqwshrlJk1hlG+m20owM4J57gAce4HrExkbgvfe4ZjLS1iMmJzNMxsfzyb5pEwOverRFREQkBilEypAlJACzZvW/5cfKlfz33r3+11D2lJ0NHDvGTkqJAG433w1YuZLvGISjZXP8eA7fufnm7h7p//5vYPfuyOt/jo9n4E1L47shmzcDBw9qexARERGJKQqRMiwTJrCjz1+RyrvlR3v7wFt+AFwbmZWlnRQiTkYG95ScOpVBsqFhZB/fGL5j8cgjDLWdndyM9He/4xCeSONy8R2RnBxuiLptG0PvSLcGi4iIiISAQqQMi3fITl2d/6VqK1YwHJ44AZw/P/Ax3W7OLSkqUjdgRHG5gOnT2eLqcjEcjXQgSkgA1qwBHnyQ7zbU13M7kI0bRz7YBsK712RuLp/Me/Zw3eS5c3pyi4iISNRSiJRhy8wEJk4ErlzxfXt6OoduWsslbf7u11NiIl9/a2JrBPJ4+M7AtGmsSra2jvw5jBkDfOITrI663QxlL74I7NvHKmUk8g7hSUzkOyqbN7OaWl2t6qSIiIhEFYVICYrp0/na3V/gW7QIKChg3ti4kUM/B5KWxnWUBw/qNXbEcTr5S1+6lL+kcPQeOxzA/PnApz7FNtuODm638fvfc0JTpHK7WUXNyeHPbdcuVidLSkZ2SxURERGRIVKIlKBITOx/yI4xwK23smp55QorkoHs1JCVBVy8CJw6FdzzlSDJyWF7a3JyeNpbAVb47rgDuO8+vvNQXc0Jru+8w3bXSGUM+7Zzc9mme+wYp7ru388/kkjaykRERESkB4VICZoJE5gl/C31cruBu+/mAMuSEq55DER2NkNkJBeXRrXERG4KOnUqg2Q42lsB9lQ//DCwbBnXbJ4+zcE7xcWR3xMdF9c9iKeqCtixA9i+nWOKI7U9V0REREYthUgJGqeTe9T3N2TH42HRyLveMZA9IR0OViQPHIi87QHlKqcTmDEjvO2tAMPjokWc4uptcS0qYpg8ezbyq3vGsJqam8tz3bu3u9U1kB5wERERkRGgEClBlZkJ5OX1nyEmTOBcFoDde1VVAx/X5WIALSoKbL9JCZPcXGDVKlYnw7GnpJe3xXXduu4prm+/Dfzxj9HzTkRiIn+eSUndra5Hj+oPQERERMJOIVKCbsYMFoD66yCcP7/7fhs3BjZPJCGBLbF796ooE9GSkthSmp8f3vZWABg3jlNcV69mH3VpKfDyy2wVDed5DYbbzVbXjAzgwgVgyxa26NbURH5lVURERGKSQqQEXWIiMHOm/yE7ALv2brqJhZb6es5ACaRolZrKwHnggCa2RjSnk5OWli7lItmqqvAFHu9mpo8+CsyZw/M4eJBbghw/Hj1BzOlkqT8nh380O3cCW7cyWLa3h/vsREREZBQxNlpeQI2gwsJCWxTo1BfxqbMT2LaNr98TE/3fr6EBeOUVoKmJ1ckbbwzs+JWVwPjxXIPpdAbnnCVE2tq4L2JJCTcNTUgI7/lcvswn58WL/HzMGLbg5uSE97yGoqWFgdIYYNIk9oqnpob7rERERCQGGGOKrbWFvm6LmEqkMWaCMeYXxpgyY0yrMeacMeY5Y0zGII+z1hjztjHmgjGm2RhzxhjzkjFmZajOXa7ndLL4U1vbf6EnJQW4807e/+BBLv0KRG4uUF6u1taoEBfHtL98OX9Z4axKAmwNXbcOuO02tt5WVPCdjK1bo2+fxoQEht+MDLbqfvABJ7tWVmqqq4iIiIRMRFQijTFTAWwHkAvgNQDHACwDcCuA4wBWWWsHHL9ijPkugP8NoArAqwAuA5gGYB0AF4D/aa39z4GOo0pk8Ozbx8yQnt7//Y4e5VIvpxN44AEWhwJRXc0csGRJ+AtcEoD2duDkSU5KTUvrv0w9Etra+E7EwYPsj46P53rOWbNYRo9GTU0s8bvdQEEB14UmJYX7rERERCTK9FeJjJQQuRHAXQC+ZK39YY/rfwDgKwB+aq39wgDHGAugFMAlADdYayt73HYrgPcAnLXWThnofBQig6e5mTsUZGRwwmp/PvgAOHyYr3c//nFWKQNx5QrDZ2Fh4F8jYVZTw4WtLS1c5xfuwFZTwxZX72ak2dlscR07NrznNRwdHfzj6Opi6T4/n3+I4f5Zi4iISFSI6BBpjJkC4DSAcwCmWmu7etyWCqAcgAGQa631O9veGLMcwE4Af7DWfszH7XXg9zvggiGFyOA6e5bzSwZactbZCbz1Fl/HZ2Wx4zAuLrDHaGhgUWnZMha4JAp0dHCj0FOnmP6Tk8N7PtbyybpjB59QACdELV0a/nMbDmu5LUhTE8v1BQUs9Ye7CiwiIiIRLdLXRN529ePbPQMkAFhr6wFsA5AEYMUAxzkJoA3AMmNMds8bjDFrAKQCeCcoZyyDMmkSX68OtNzM6eTWfunpbIF9993AJ7CmpPAxduzg9oQSBVwu7vOyahV/+ZWV4Z0yagwwZQrwqU8BixfznI4fB377W2DPnuhdfGsM/0Byc/lHcuIEsHkz8OGH7AfXmGMREREZpAEaDK9njLkBwAIAEwC4fdzFWmv/eRCHnHn14wk/t58EW11nAHjX30GstdXGmMcB/ADAEWPMq+DayKngmsg/Afj8IM5LgsTp5FyVXbsGXreYkADccw/w6qvA+fMMhatWBfY4iYns1NuzB1iwAMjLG/65ywhISwNWrgTKyrg41trwtl263aw+zpjBJ+3Zs1w3efQow+Xs2dE7EtjtZpnfWk698v5RTpnC6qQWFouIiEgAAg6RxphMAL8CcI/3Kj93tQAGEyK9zYe1fm73Xj/AaBbAWvucMeYcgF8A+FyPm04BWN9znWRfxpjHADwGAJMmTRrooWSQsrIY6ioq+O/+pKUBd90F/PGPwKFD/HzevMAeJz6ex9+3j4Wj/HwWYiTCORzcniInBzh9mtuBJCWFd5Gr94l48SLD1sWLXDd56BD7pgsKovfJZQy3AklN5R/KsWPAkSOsVk6axHWq0RqURUREJOQG81b/cwDuBauBnwVwJzg9te/lNn8HGCLvq7QBF28aY/43gJcBrAcrkMkAlgA4A+DXxphn/X2ttfZn1tpCa21hTjTuFxcF5sxhLqivH/i+48YBN9/Mf2/fzqpkoFwuZpGjR9mNqG69KBIfzyfKqlWsilVUAK2t4T2nsWO5QPfuu9lrXVsL/OlPwGuvcZ+ZaBcXx0FCOTlcC1pUxF7yo0cH3qNHRERERqWAB+sYY6oBHLHWrg7qCRjzLwC+BuBr1trv+7j9RwD+F4C/sdb+Wz/HuQXAJgCvWGs/0ee2JLBddhyA6dbaM/2dkwbrhE5jI4s5KSnMCwMpKgKKi9mF97GPDVzF7MlaLrMbMwaYPz/wIT0SIaxliDxyhGslMzLCXx3r6mLVrriYg2oAYPJk7oGZMagtbSNbZydQV8efe1ISq645ORrGIyIiMooEa7COE9zLMdiOX/04w8/t069+9Ldm0uv+qx839b3BWtsEYDf4/S4a7AlK8CQnA4sWceeBQPZCX7IEmDqVr2U3bOh+3R4IYxggq6uBnTu7B25KlDCGVcCbbuKToKqKT5xwcjhYKX30Ue4p43az9fall4BNm1i5iwVOJ0Nxbi6/x6NHOYynuJiTqzo6wn2GIiIiEkaDCZF7AQy4x+IQeEPfXcaYXudzdYuPVQCawe07+uOta/nrRfVeH6UjFmNHTg73cg9kiqoxwC23MAw2NAAbNw7+9WtmJota27axLPpFTAAAIABJREFUMilRxu0Gpk0D1qzhOsWKivBOcfWe05IlDJNz5/KJeuIE8LvfcWPUQHq2o0V8/PXtru+9p3ZXERGRUWwwIfKfAdxvjAlqO6u19jSAtwHkg22rPT0Frmv8D+8ekcYYtzFmljFmap/7br368TFjTK+5nMaYe8Ew2oLQVFNlkAoKOEelqmrg+7pcXI6WmsoQuGnT4F+3pqQAHg8nt54+rde9USk5mcFtwQK2Woa7Kgmw1XP1auCRR7inJMB21xdfBD74ILbK3z23CklP5zTd7duBLVu4aLm5OdxnKCIiIiMk4DWRAGCMeRDAzwG8AVYmffZuWWv/Y1AnwUC4HUAugNcAHAWwHBzUcwLAjdbaqqv3zQdwFkCJtTa/xzEcADYCuANAPYBXAFwEMBtsdTUA/tZa+/xA56M1kSOjvZ1DLzs6GBAHUlPDrT/a2oCFC7kMbbA6O4HLlzkpds4cFpQkCjU3sxJWXs5Sc6QseL1yhduBnDrFdyqcTj7RFi5k4IxFra2svHZ1dU93zcjguz8iIiIStfpbEzmYwTpxAH4K4C/RXcHs+8UG3Cdy0NMvjDETATwNbiGSBaAcwKsAnrLWVve4Xz58hMirt7nBauajAOYASAJQDa6HfMFa+3Yg56IQOXIGO2jnwgXgrbf4enX5chalhrLLQlVV9/rMWH1tH/O8g3cOHuSTIJIG21RXc/3gmaszvFwutr0uXBi7ezFayz/opiaG57w8YPx4tiCHa89PERERGbJghcjvA/gKgCMAXgRQBsDn6jRr7S+HdqqRQSFyZF2+DOzezWVXgQzfPHaMy84A7h+5cuXQXqPW17Maunjx4Ka+SoRpaeGToqyMbZaBvBsxUi5f5hrCkhJ+7nIBM2ZwXHD6gFvfRq/OTv6BtbWxSjxhAockeTzRu7emiIjIKBOsEFkK4DKApdbamB5OoxA58s6cYQ4YMyaw+586xWGRnZ3AlCnArbcOrXuupYWzQebNYxeeRDFvVbKriy2ukRRWKitZmfRueGoMn3ALFjBcRdK5BltHB9ewdnRwi5DJkzmkJyUl3GcmIiIi/QhWiGwE8BNr7deDeXKRSCFy5FkLHDjAHBBoVbC0FHj7bRY7xo8H7rpraEWojg4WjBYt4nEkirW2ckrq+fOs9EVa62hNDZ/oJ09273GTkwPccAOnTYV7H8xQa2tjoOzqYlXSGygjqXosIiIiAIIXIosAHLPW/kUwTy4SKUSGR3s721rb2wMbtANwbeNbb3EpVmYmcO+9QytwdHTwWEuX8jWtRLlLl1iV7OiIvKokwHWDR44Ahw+zHA7wiTtvHve/GQ2hqqWleyuUMWO6B/Jo/aSIiEhECFaI/DNwMutia+2JIJ5fxFGIDJ/GRu4akJQUeBGpvh54800OxkxOBu67j7lhsFpbWSRZsSK2l6uNGm1trEqWlERmVRJgyD1xgoHXu2VJXBzXTN5wQ+RMnQ0la7kVSnMzxyVPnswW30DfSRIREZGQCFaIXAPgSwBuB/A8gGL43+Jjy9BONTIoRIZXTQ2wcyeLEoFuwdHSAmzYwHbY+HjuKzlu3OAfu6WFRaKVK7VkK2ZcvswW0kitSgIMUufP8zzLynhdQgKnPs2ePXq2y+i5ftLjYYtvVtboqMyKiIhEmGCFyC5wSw/vKzC/XziULT4iiUJk+JWVAfv2BT6xFeDrznffBc6d49fcfjtfgw5WQwOXbK1YwTkgEgPa2jiN6ezZyK1KepWXcwPVigp+npoKFBYC06aNrlZPb7urtQySeXl8Z0l78oiIiIyIYIXIJ9FPcOzJWvtUwGcXgRQiI8Pp08Dx49y/PNDiUVcX9508coRfc+ONXGY2WHV1LP4sW6YiSEypqmK1r709cquSAINTSQmwZw/3nAR4vkuXst0zUs87FKxlq2tTE//AU1O5ZUhWFtsFRtPPQkREZAQFJUSOJgqRkcFazh25cGFww26sBT78kK+/AXYD3njj4DsCvWsslywJvK1WokB7O6ejnjsHpKVFdlWyq4sV1KKi3kNoli8fWr92LGhp4eLpzk6+w5OXx3eaPJ7Yn24rIiIyghQiB0khMnJ0dgJ793Ivx4yMwX3tiRPAli08xpgxwJ13MhQORlUVCx4LF+r1acyprgb272era2ZmZLeKdnRwI9W9e1mVA4CJE7lmcuzY8J5bOHV0MFy3t/P3l5nJP/a0NFYp9UcrIiIyZAqRg6QQGVna2jhox9vJNhiXLnEvyYYGLqW6887Bv+a+dInFjrlzIztnyBC0t7Nv+swZPrkifb1dWxsnue7fz3MH+ORcvFibnPZse7WWf6xZWaxSekOl/oBFREQCFqw1ke8F+HjWWnt7oCcXiRQiI09TE9c6DmbrD6/mZuCddzisx+Fga+ucOYEvpbKWQbKgAJg5U0uwYlJNDddKtrREflUS4HkeOMB+77Y2XjduHMNkXp6epADfdWpu5sUbKrOz+XPKzNRiZxERkQEEczprf7yTW62ms0ooXLkC7NgxuK0/vLq6OPDywAF+PnMmsHp14OskrQUqK4H8fGDGjNGz48Ko0tHBiuSpU6xaDbb3ORxaWhgkDx7kRqcA2zkXLQImTVKY7Kmri+9GeUNlRgYH9GRmRn4FWkREJAxC2s5qjEkDsBTAdwGcAPAX1trOYR00zBQiI9fFi0BxMQftDGW508mTXCfZ0cEutzvvDHw/SGu5RjIlha/R9bozRl25wlDW2MiAEQ3r6traOJL4wIHuNZNZWaxMFhQoTPrS3MzfsSa+ioiI+DQiayKNMZkADgF4zlr7bFAOGiYKkZHtzBnOGBnM1h89Xb7MdZL19dwH8s47Bzfosq6Oy9EWLuQ5SAzq7OSekidP8t2CQN9pCLf2duDoUYbJxkZel5nJdz2mTIn8Nt1waW3lfwhdXeyXnzCB71R5PPqZiYjIqDVig3WMMf8XwBpr7YygHTQMFCIjm7V8nXzuHF/nDeU1XksL10mWlvLrb7oJmDUr8K9vbWXBavp0YOpUvc6MWXV1bBetqWEYi5a9Xjo6uMnqvn2cKgUA6emsTOoJ27/2dgbKjg7+vsePZ4twenp0VKVFRESCZCRD5A8A/I21NoI3XhuYQmTks5ZFopMnOStjKGsUu7qA3bs56NIYViQLCgb39ZcvM8jOmxfZ2w3KMHR1AeXlbBe1lmEyWtodOzu5182HH3bvM5mWxsrktGkKRQPp6GAIb2vjz2rsWLYtpKdHzxsKIiIiQzRS7ayJAPYBSLTWTgrKQcNEITJ6lJQAhw5xKdNQX9MVF3Mvd6cTuO++we+UcOUKM8WiRYPfy1KiSGsrtwM5dy56Bu94dXbyHZd9+7jpKsBWzUWLWE5XmBxYVxdbhJub+Qefk8P/LDTpVUREYlSwprP+Tz83uQBMBPDnAKYB+J619vGhnGikUIiMLhUVDIJpaUOrBlrL7UMOHwbi4oAHHmB1czBaWvjafO5cDcWMeVeu8MlSV8cAEU2jeru6OH12797uMJmSwgW+M2aouhYoa7sDpbX8DyMvj88HtSSIiEiMCOYWH77u7H253AXg1wD+P2tt+1BONFIoREafmhpWE+PihjYDpasLeO89FpqSkoB16xhKB6Ojg9Nb8/K4D6Vej8ewri5uPHr0KD///9l77yjJ1rO899nVOec002mmJ88JMzpRRxkFBBiuAJt1MeCFTbAwJhnwRUYGJN0rAybZEvYV6bLuhQsGX4EMIggJoaNzNCefORN7OndP7OkwnSvt2t/94+nPu7pPT0+HqtpVtZ/fWntV9a7qqm96dvie733f521qKqyVA8+jQ9Vrr/HkAXjyHD0KnDzJ0L7YGcb4Tq/GMNW1t5eCsqoq6NEJIYQQeyZTIvJ7sbWI9ADcA/CKMebOXgeZT0hEFiYrK8DLL3N+3Ni4+993XeBv/oZmOw0NFJJ7aeMxN8ffO3u2sDIexR6IxRjZm5oqvBRXgKJnbIwtTaan/f0dHRSTAwOFFWnNB9Jbh9TXU1A2NPD4kKGREEKIAiJnxjrFgkRk4RKLMbV1dXVvwZREAviLv6BhTmsrU1vLy3f/OSsrLKE7e5alU6LIuXePxjuLi4xK7uWgCZq5OUZWh4d5IgCs9Tt2jIJSBb+7JxbjxcDzWHfa3My+QFZUqhZVCCFEHpOpSOTfA3jeGPPvMzm4fEQisrBJJtkm7+5dCrjdZhlGo8DnPkc9cPAg8MEP7i0YE49TW5w4wRZ9hZTtKPaAdXG9epVGNs3NhRl5SiaZ1331Kk8iS1cX87TVb3JveB4vLtEonzvORlFZV6eorxBCiLwiUyJyDcBvGGP+XSYHl49IRBY+qRTnwFNTjCjudsF/aYlCcm2Nc+b3vndv8+ZUilHNri62AVGdZAhIJoHxcaa5VlTsLbc6X5id9aOTyfVS97o64OGHgePHCzPimi8Yw0ilraUEeKx0dkpUCiGEyAsyJSLfAHDBGPM9mRxcPiIRWRzYcq9r1zg3260L/9wcU1vjcQZg3v72vUcT5+bosXH27N6Mf0QBsrLCg296mrVxhWyykkhQFF+8SHdagCfUyZO0JNZBvX+sqFxb8yOVDQ0UlY2N/BtLVAohhMghmRKRPwLgYwDOGGOmMji+vEMisriYmWF7vNJSzuV3w+3bwOc/z4jiY48Bj295Gu2MlRXOEc+eZQabCAlzc2wJsrpaeC1BNmMMm7NeuMCTA2CY/8gRRifl6po50iOVnsdUiMZG/o3r6+neVVWlukohhBBZI1Mish/AfwZwFsAvAXgZwB1s4dha6CJTIrL4WFsDXn/dn8fvJqI4MQF84Quc0z38MPDUU3uftyUSwPw86yQPHVJpWWhIpWj7Ozjot4EoZDEJMMJ68SLD/fY+0tMDPPIIi4lVBJxZrKiMxZhabP++NTU0PWpq8oVlRYX+/kIIIfZNpvtEOti61YfFGGMKenYkEVmcuC7n8JOTrJPczRx+aAj4ylcYEDhwAHjf+/aenZhKMTjV1MSyMplehohkkmJyeJgHQlNT4YvJpSWKycFBnmQAV2pOn2bfSRUCZ5dEgsIykaDQNIa1qm1tTHmoq6O4lKgUQgixSzIlIn8f24vH/4kx5p/veHR5iERk8WIMcP06swvr6nYnBO/cAf7u7xjVrKkBPvCB/aWlrqzws1pbOdcuZP8VsUuSSeDWLa5OpFL8zy90sRWL0YTn0iUe2IDfIuTUKR3gucR16QIbi/misr2dwlKiUgghxA5Rn8hdIhFZ/CwssJ8ksLu57eoqheT0NFNa3/52pqbuh+Vlzrk7O1lattu6TVHAuC5rC4eGGElqaip8MZlK0Z328mWuvFh6ehid7O2VgMk19xOVra1+CqycdoUQQmxCInKXSESGg1iM/iBzc7vrJ5lKAefOcY4M0KDymWf2n5W4tMQxdXVRTMrwMkS4LgWXFZPFEJkE2CLk8mU/fRfgKsnp04xQVlYGO76w4rpcuUpPga2s9I17amooLisrJfiFECLEZCqdNQXgF4wxn9jmPT8L4GOqiRSFQirF+e3oKBfmd2N0c+0a8NWv8jM6OlgnuV/hZwywuMi5XU8Pe1RWV+/vM0UBkUoxMnn1Kn9uaiqOSXwsxprJK1cYege46nLqFI14amqCHZ+gsLTGPXZeYB1hm5v5WFMjYSmEECFiOxG5G7HnrG87eZ8QBUFJCdNRIxG2wWtv3/n86Phxzq2+8AWmt372s8D7389I4l5xHM7VjGFg6vp1RiUPH5aTfygoKQG6uxkaHx4GpqYKv8ckQOFx5gwF49QUo5M3bjAV4PJlnkyPPqpc7iApLeUqWPpKmOexUe7EhG+aVFLCaGVrq19fudsmvEIIIQqe3bqz/oIx5uPbvOdXAPyQMaagl5UViQwfxnAue/367s1yolHgS1+i6WYkAjz9NPDQQ5lZrE+lgHv3qCEeflhOrqFjft43qmluLq6VhNlZ9t0ZH+cJGIlwxeTsWZnw5DOpFKOV0ejGVNi2NhZ2NzQURyq2EEKIvaezOo7zzrQf/wHA769vmykB0AvgFwHcNsa8ZY9jzQskIsOJ5wHnz3Nuu9ue6Z4HvPQS8MYb/HlgAHjXuzI3l1pbYxZgf7+6JoSOVIp9aa5dY8SnoSHoEWWWe/d44o2M8ERyHDZRPXuW0S6R/7guXcficf7/tbSwV2hjo/LxhRCigNmPiLS9IXf0PQA8AP/MGPP/7nqUeYREZHhxXbq2Li/vLeo3Osp+kskkf/8DH8hcUMUYBqZKSxmVbGvLzOeKAmF1lbWSd+/y4Co2N82lJa7CXLvmm/D09vJgP3BgdwXLIjiM4apXNMpFgZoapmi3tDD9Vf+PQghRMOxHRP4CKCIdAD8HRiO/ssVbUwDmAHzZGDO4z/EGjkRkuEkkgBdfpBDcS9BnYYF1kvfuMWL4rncxMpkpYjGa73R3y+AydBjDAtxLlzhBb2oqvkn56iprJa9c8evwamoYgj96lGm9onCIx9kUN5XiBfHAAT/ttZjSs4UQogjJlDvrOIBfN8b850wOLh+RiBTRKPDCC5yf78VxNZlkRHJ0lD8/8gjw5JOZmzMZQ7FqDOsvOztlmBgqEgmmf05OFmeKK8DVkkuX2PbEOroCTHE9dowrM0qVLCxcl4IykeDF8MABOpE1NOy/R5IQQoiMoz6Ru0QiUgCc65w757dL2y3WrOfcOQaNOjvZBiST3QySSaa4trXRZbauLnOfLQqA5WWmf9696ztlFhvWqnh4GBgbY2QL4ApPdzejk/39EiGFRirFi2w8TkHZ2UlR2dio/0shhMgTMhWJHADwNgCfN8bMbfF6K4BvBPCcMWZsH+MNHIlIYVlYYESyoWHvJWh37gBf/CKz9KqqKCQPHMjsOJeWGLjp62OARo77IWNujvWSy8uchBdbvaTFddkiZGiIVsqex/3l5Wys2t/PR50AhYXn+YLScbgq1tHBhZGaGqW9CiFEQGRKRP42gA8BOGCMSW7xehmAmwD+P2PMD+1jvIEjESnSmZkBXn6ZvhB7XSDf3AbkiSfYFi+TKaiexzpMgNl+PT2ae4UKzwNu3wYGBym2mpqK+wCIRpkvPjzMSKwlEmGKZH8/t73ko4vg8Dwa88RijEIDXBhpbeUxXVOjQnAhhMgRmRKRwwBeMcZ85zbv+UMAjxtjju9ppHmCRKTYzM2b7ELQ1rb3ebnnAa+8wtZ4ABfan3wy81FJ16WYrKgATp1i30vVS4aIZJLRuuFhrno0Nhb/AbC4yPrQyUkK6fT7WmsrQ/T9/VwJKva/RbFhDCOUa2tMgbV9KVtbudXXM41b/69CCJFxMiUi1wD8J2PMR7Z5z38A8K+NMQVdmSURKbZiagq4eJGL4fvJlpuYAJ59loEUgGVdTz6Z+ZYd1sW1pYX1ksXovSK2IRql+c7164zehCUiF4vxZJ2YAG7coKi21NcDx48zVB+Wv0cx4roUlbY+tqyMF9C2NolKIYTIIJkSkQsA/tAY88PbvOfTYJ/I+j2NNE+QiBT3Y2aGkcSKiv3NQZNJCtI33qBRIQAcPsw010z1lbSsrLAes6cHOHKEdZkiRCwusl3GvXvF2V9yO1wXuHWLgnJyksIDoMDo7eXqivK+Cx/X5aKJTYEtL6egbG/3DackKoUQYtdkSkQ+B6AbwNH71ESWAxgCcMcY8/Q+xhs4EpFiO1ZXgdde43xlvy3rYjGmyV66xEytSIRBkscey2ygxLYE8TymuKp3e8iw/SUvXw5HveRWeB4F5dWrFJSpFPdXVzM6efy4wvXFwmZRWVrKY76lhZHKmhquBEpYCiHEtmRKRH4YwH8B8DkAP2SMuZP2WieA/xPANwP4EWPMf9n3qANEIlI8iGSSwu/2bS5471eQraxQmF67xrluSQlw+jRw5kxmI4euy5YgjY3sL6mWICEjmWRUbmSE0ZpMh70LhWiUDq+Dg1xdsRw8yOikWoYUF55HQRmN+o6+ZWUUlq2tfrRSrr5CCLGBTInICIC/BfBeAGsALoBurAcBPAKgGsAXAXzQGONlYNyBIREpdoLn0RxyaIgL3GVl+//MhQWa74yO8ufycqa4njqV2cjhygoz+44cAQ4d0nw5dKyucsXi9m2/hiyM2Ajt4CBPOtfl/ooKnhzHjnGVSBGr4sN1KSzTXWDLyhiNbmrieVFVxS1sUXshhFgnIyJy/YPKAHwMwA8BSM/7WQCjlB/bKtW10JCIFLvhzh2mpNbUZG4uPjsLvPQSPVEAlva84x1cNM8UnseoZEUF8PDDFMIiZMzPM8V1eTl89ZKbiccZoR0c5AloaWqimDx6lCe5KF5SKR4H8bhvyOQ4/H9vbORmU2ErKrT6JoQoejImItM+MALgBIBGUEAOFnr0MR2JSLFblpaAV1+lMMtkhuDEBPDccwwcRSJMQX388cxEPS3WxbW7m2VhyugKGbZWcHiYB0N1tZxL5+aYYjA87NsoRyI04Tl2jKY8EhDhIZHwxaXn+ZHpigqmwtbX87Gy0heYil4KIYqAjIvIYkciUuyFeJxuq3Nzmc2ASySY4nrpErOuamuBt72NZVuZwhiadwJMne3qkvFO6LCh6bExHsQ2tS/MB0IqxXSAoSGa8dh6OpvuevKkQvhhxnV5gU4kNraSAbjIUFbGY6W8/M2PJSX+6xUV4T7PhBB5S0ZF5HpK63sBnARQa4z5xPr+SgD1AGYLPSopESn2SirF+ebYGJ1bM5kdODPD/pI20+7QIeCZZzIbNEomKSbr6ykmm5oy99migFhepniy+dQNDZkNfxcitu/mtWsU2ZaODp4shw8rOil8PI83hPTN8/y6281zr5oaXngbGpgNUFnJLcwp5kKIwMlkTeQHAfwugE4ADgBjjClZf+1pAM8D+G5jzB/te9QBIhEp9sv0NKOSZWWcF2QKz2MJ28svU/CVlQFPPpl5453VVZrvdHaqL3uoicdZ9Ds2xlTX2trwmvCkMzvL2snhYb/Ra0UF88FPngyv663YG8bwgm6jmrb9DMCIpXWRbWjgORj2BR0hRM7IlDvr4wCeAzAL4JcBPAngO62IXH/PCIDXjDHfse9RB4hEpMgEq6sUkktLvP9n0uBxZQV4/nnWTALMqHviCZZqZfJ7FhY4pzl0iJvqJUNKKsVU1+FhFtDW1zNKEnaSSbq6Xr680Yzn4EGKyf5+1caJ/WHbk6yt+fWYtbWsmWhu5vPKSjkICyGyQqZE5OcAvAPAKWPMHcdxfh7Az20SkX8E4C3GmOMZGHdgSESKTJHN9FaAIvL55ykqAUYOn3ySNY2ZwpbKRSKMSnZ3a14cWoyhWLp6laskDQ1aWbDMzABXrjDl1aYsVlXR1bWnhyen0l1FJojHmV6dSFA8lpdzJdH2uywv97eyMglMIcSeyZSInAXwP4wx/2L9561E5C8D+LAxZtcJfI7jdAP4OIAPAmgBcBvAn4NtQ+7t4Pe/F8D/9YC3eenjvR8SkSLTZCu9FeB89coV4PXXuWANcM765JOZbQniuqyXrKhg+mx7u+YmocXz/P6KsRhXSCSQSDzOiO3Vq1x9sZSVAQcOMF2gp4cTfiEyge15aVNhjfEvzo7DSGVNjd+eZHONpn2eSvFnY7hSWFHB362q8lualJXxMX0TQhQt24nI3Zz9tWAq63ZUA9h1ZZbjOAMAvgagHcDnAAyC6bI/BuCDjuO8zRgzt81HAMB5sIflVrwDwNcB+Ovdjk2ITNDRQUfVN95gwCKT6a2lpcAjjwAnTgAXLgAXL/qeKAMDbAmSiRKt0lJmUMViwGuvcQ586BD3yfshZEQiDHe3tbE9yLVrnHg2NGhSWVHBXjynT1NoT0zwZJyfp8Pr5CTf19joC0pFKcV+KC29f+G6MRSG0ShrK1Ipnr+Ow80W00ciG5+nUnz/vXu+6LQ4jm8MFInwmLdCs6qKm42Clpb6DrRCiKJiN5HIUbAf5Det/7xVJPI5APXGmEd2NQjH+VsAHwDwo8aYT6Xt/zUAPwHgM8aYD+/mMzd9/jkATwP4X4wx/+NB71ckUmSLbKe3AhR5r7/OMi07Xzh2DHjsscwa5MRiNPF0HAZYurs5L1Z0MoQkEhRKIyM8AJqa1LJgMysrwI0bwNQUcPOmb8gDcKLd3Q309XGrqgpunELshnQXWtf1t63cZ9vb/bRbiUohCoJMpbN+GsCHAbzbGPPcZhHpOM43APg8gF80xvy7XQzuMIBRABMABtLbgziOUwemtToA2o0xqzv93LTPeAjARQA3AfQZY1IP+BWJSJF1pqfZ9zGZ5Hw7G0GIlRXg1VcpWj2P2UlnznDL5Pd5HsVkPM7Mp8OHOVfQPDiExGKMvE1M8IBrbJSY3IpUihcBmzKQ3jLEcZi60NdHYx45vYpiIL2OE/BFZWurbw6UKzyPY4nFeH2yabplZSr4F2ITmRKRBwG8AaasfgpAP4B/DOBbALwTwA8DWATwqDHmQWmv6Z/7/QB+G8BvGWP+5Rav2yjl+4wxX9rp56b9/qcA/GsAHzfG/PxOfkciUuSCZJKBieFhLto2N2dnvr2wALzyCk0kAc5J3/nOzJrvWBIJZkB5HucHfX3Z+3eJPCYaZdrm+LgvJjU5uz8rK4xQTk4ySpmeOtjQQDHZ38+TSieTKAYSCTrOWnOgqioaBlRV+X0yrbDbj0FQIsHr0doab4b37nHV835z35ISvw60qorPq6s3jqO8XNczERoy2SfyLQD+BMDhtN0GjBSOAvg2Y8zFXQ7uPwL4KQA/ZYz51S1e/zQoUP+VMea/7vKzqwDcAlAPoN8Yc30nvycRKXJJPM7AzdgY70/ZSgm9fRt49lneRwHWUD71VHYWgI2heefaGj//+HHOf1X2FTJiMV9M2jRXTb62J5Hg6pKtn4zH/dcqKlg/2dHBE6q9XT0DRXGQSHBlNZncOh0W8GsrS0v9es702s70LRr1e1RZysv9z7jfTdaaDdnNpuqmj8cYnnfV1RRqYR7UAAAgAElEQVSaNTV8bm9wmz87/eeSEq6squ5DFAiZMtaBMeY1x3GOA/gmAG8FXVQXAbwA4HPGGHcP42tYf1y8z+t2/15yer5j/fc+/yAB6TjODwL4QQDo7e3dw1cJsTdsj/KeHpaU3bxJ4ZVpF9euLuDbvx04f57b4CDnqG99K3DkSGbvabaVWW0t58AXLvD+evQo6yc17w0JdgWhr49pm2NjPDgUmbw/5eXMCT98mBPaO3d4ok5MMMyfbs4TiXBC2tHhb3V1mqCKwsO2JNkOK+ySSQq59M1if45EKO4aGu7/eVsRiexsLLYGdGmJplm2rU/6WNINiOxzz6Oh1smTugaKgmdXkcisDMBxfgvADwD4AWPM72zx+icBfATAR4wxv7jLz34ewDMAvsUY8xc7/T1FIkWQLC2xjvHuXYqwmprMf8fCAvDVr9JYE6Cnx9vfvvv77W5IJtmnPhKhq2t3t/rVh454nGmb4+P8WWJy5xjDNLy7dyksp6c5efW8je+rruZKzcAATzKF/4XIH2yv3dZW4NFHtaIq8p6MpbNu+tB6MIq4aIxZ2sfgspLO6jjOKQCXAdwAU1kfaKhjkYgU+cD8PKOFCwsMNmT6XmMMOzO88ALn9iUlbAfy8MPZnde7LsWk5/lmlNXV2fs+kYfE435BsM3hFrsnmWTPoOlpf7PNYgFGU/r7GdXs7pZgFyJfmJ/nje+xx7SaKvKajKWzOo5TDuCnAfwL0FjH7p8A8HsA/qMxJrHlL9+fa+uPx+7z+tH1x6Fdfq416fnd3QhIIfKF5mbg6adZy3jlim++k6lMNcdhXWRfH3DuHOfzL77IKOiTT3J/NrLiSkvp8u55TN2dmPB7sKtFSEioqGCkrKuLKxm3bzMNU6sJu6OsjCfPgQP82Riu0ExM0ElrdpYn9NAQ/+ZWUB48KEEpRJA0N3OF+Nw54IknMtt/S4gcsRt31joAXwLwGGimcx3AHQCdAHpAc51XALzXGLOy4wE4zgCAEWzf4iMCoG2nLT4cx6kEDXUasAtDHYsikSLfiMc5J5yYyF6K640bwHPPcQ4K0L/jqaf4mE2MYQqvbRHS38/SLumJEDE/z8amy8tcYVAKZmZYWGAd6tjYxjYiVlAePAi0tTGPXas3QuSelRWa/zz+OI3HhMgzMtXi49cA/DiAzwL4aWPMeNprhwD8CoBvBfDrxpif3OUAbRuPHzXGfGrTd/4EgM8YYz68vq8MwACApDFm9D6f9z0A/m8Af2mM+ebdjAWQiBT5y8IC59pLS1zIzPRc23WBq1eB117zs+L6+rhQ2tKS2e/aCls36Xn8vr4+aYrQkEqxSHdwMPNhd8GLx+goBeX8/MbXKip8t1e7KcVOiNwQi/HG99hjXEEVIo/IlIi8DmDGGPOW+7zuAHgNQKsxpmeXAxwA8DUA7QA+B+AqgKcAvAdMY33GGDO3/t5+AOMAJo0x/ff5vK8CeDt2aahjkYgU+YxNA716lSY12UgBTSToqHrhAoWd49DB9fHHM+8aez9WV7mVlLCc68ABBUxCQXrYvaZGaV7ZYH6eDq/T06ypXFt783vq6ykmOzpoHZ1N1y0hwk4yyWyBhx7i6qkQeUKmRGQUwG8YYz6yzXt+EYwm7joRzXGcHgAfB/BBsHXIbQB/DuBjxpj5tPf1YxsR6TjOSQBXsAdDHYtEpCgEYjG2BJmayl45WTQKvP46azJTKQq6kyeBs2dzl26aSjHLMZGgYD59OndCVgTI0hJXSqwBhcRkdrBNXa2gvHuXj+6mjl0NDRSTvb2sZVV6gBCZJZXiuXfkCPthRSJBj0iIjInIqwD+wRjzQ9u8578CeJcx5tSeRponSESKQuLePYq8pSXOs7Mh7paXgVdeofmO7bN84gS35ubMf9/9WFlh0MS20ZM7epFjDFfnJyY4uSotpZiRKUx28TyK95kZpj3cuMEIsaW0lKkBVlRqVUeIzOB5NMRqbvbrOXSjEwGSKRH5bwD8LIBHjTE3tni9F8B5AJ8wxvz6PsYbOBKRotCwc+2hIZY+ZSsLcH4eeOklv9c5wGy348dptvmg/syZwM5vy8qY+dPWphTXULCyQkEzOckV+/p61e3lCs9jhPL6daY+zM5ufL2hgdGTEycUMRYiE6yu+mnmXV00wWpq0gKayDmZEpH9AH4DwDPrj88CmAbQAeBdAH4MwPOgEc6G7sfGmKm9DT0YJCJFoWIMI5NDQxRa2RKTs7PMNBwZYZopwODEwADnkR0d2Rd21ougs5PfKTfXkGB7I46McKJVUUFBqZWE3LG6yujk9esbo5SRCF1fT5/mxFf/J0LsD8/jAlosxptsdzfPLRkEiByRKRHpga09nPXHN73lPvuNMaagiickIkWhYwwjksPDFHzV1aybzDSuS7PHwUG2+rM0NlLYHTsGVFVl/nvTWVigrjhxgtl1WqgNCfYgn5qiq2skwoO8oiLokYULz+PJf/UqMD7OnwGm450+zdoupeMJsX9c128JUlnJVPL2dq4US1CKLJEpEfn72FokPhBjzD/fy+8FhUSkKCYWFhi0uXs3u2aXCwvsGz805GfhlJRQSJ49mx0Ra3FdRmBrazlvVbutkBGN8gCfnGSUrLSU0UmZv+SWlRWuKF296l8EysuZ737qFFeXhBD7J5mkEUIqxZXanh7WdtTVSVCKjJIRERkmJCJFMbK4SAOehYXs9Ji0pFLMchscZJDIGAaJjh6lmMxmp4C1Nb9ffV8f0NoqHREqjOEBMD3Ngy+RYGSyrk5Oh7kklWJU8vJl4M4df393N/Doo6zv0kRXiMyQSHABx3UZoezpYYRSglJkAInIXSIRKYoVz2Pm35UrjBJmOzCwsMAWIdbV1YrJM2ey+91ra7yn2h6TBw+qbC50eB4PwJs3edB7HkPxNTVBjyxczM5STI6M+G1D2tu5otTXp5NSiEySHqGsrOQNsK2Nz8vLtZgmdk3WRKTjOG0A3gbWQ54zxtx5wK8UBBKRotiJRpl6evMmxVy2TS4XF30x6XmcNx45wnlkNlNP03tMVlcDhw5x/ipTz5CRTDLfeWKCNsYy48k9sRjTXC9e5AUI4Ml/9iwduTS5FSKzJJO8Abquf62rqOBCWl0dHysrua+8nI86D8Um9iwiHcc5A+DbAHzWGHN+02vfB+BTAKyLgQvg3xpj/lNGRh0gEpEiLMzMAJcu8V7T1JT9+8fSEsXk0JAvJg8fZmSytTW73x2P837qeRSSfX1M69U9M2QsLTHV8tYt5jo3NuogyCWuy1z3N95gugBAQf/ooyygVv65ENnDdXnDTya5upquASIR1oK0t/OcrK2VU53Yl4j8FbB1R7cxZjpt/yMAXgVQAuA5AMsA3gMKyncYY76WueHnHolIESaSSTqsjo0xWpeLNm/Ly8D584yGplLc19LCOeSRI9lt12GM34KrspLf19kpA8nQsbpKI57JSU6e1IMtt6RSTE04f56pCgBP/EceAU6ezE3TWSGEj+cxSyAa9Vd5m5rYs6uhgZMD3ShDx35E5Ln19zy9af9vAfh+AJ8wxvz8+r63A/gKgD80xvyzTA0+CCQiRRhZXGTp0uIi7xu5uFesrDAgMTy8sdVcdzdrJ/v7sxuYSCT4741EGJns6VG/ydARi9EJanycKwwNDZoo5RLP49/+9deZagzwpO/pYf55b6/atggRBMbw+ri25q/21tdzs/Xl5eX+puvm/kil8nIhcz8i8gaALxpjvnfT/jEAnQBajDHRtP1/B6DfGHM0EwMPColIEVas8c61a4xQNjbm5r7gujTTHB7mo201V17OeeSxY9ntXZ5KUUy6LqOS/f38t6tkLkQkEux3ODzMg7++XsWzucQYivnz5zc2nY1EgAMHeFL298sYSYggicX8dFgrLC0lJX46k+0nVlHhb3kokAJnbY2LZ1NTXDx76qmgR/Qm9iMiYwB+1Rjzs2n7mgHMAviKMeY9m97/GwB+0BhT0Gv5EpEi7Lgu53HXrvF5U1PuSpViMRo5Dg+z9Z+lro61kydPZk/c2Q4RsRg1xMAAje107wsRrssDb2SEKa+5yvEWPisrNEGamOCFyK4qAUyts4JSfSeFyB88zxeYyaTvxmx1RkUFr6V1ddwqK31jnzBFMdOF49ISF8oqK/l3eve7gx7dm9hORD5oWpgE0Lxp39n1x9e2eP8qAPUMEaLAsdlkXV10cB0a4qJjLsRkZSXw0EPcFhb43cPDFHdf/Sqfv+MdNMXJNI7jZ+tEo8ywq6xkr/SODonJUFBayshXVxcdXcfGKCpLS5nqqoMg+9TW+heBWIx1qxMTjFROT3N78UUWUh89ysJmRSiFCJZIxI86boXr+hkftom0payMwtLegNMFZjFcc6NRXzjaGpraWpoYAfzbRKPbf0Ye8qBI5OsAatPTUx3H+SUAPwXgfzXG/Omm9/8egHcZYwayNN6coEikEBtJJn0xaQwDALk0UTQGGB0Fzp3jIl5JCc0cz57N/jjicV7zKysZBW1vl5ln6FhZoYCZmuLPqpsMhmQSuHGDNZRTU34hteNQ+B89yvx3mfIIUVhY19h4nM/TtUlVFQWmjWJacZkPAjOV2hh5ta63sZhvUhSNcuLiOP74N2NFZIFFIh8kIj8O4KMAfgfApwEcA/C7YASzyxiztOn9VwDcMsa8L0NjDwSJSCG2xopJ2++xri63nhfxOPDSS8CVK/y5oYFRyYMHs//dsRgzT2pqGJlsa5OYDB12FX1sjAdEXR0nOCL3uC6FvS2ktvVZpaV0yTp6lA5dQU8yhRD7w3V587dCLV232J6/dXUsPTCGm+dtfA7w0fN44y4t5bXBbpEIN8fxH1MpP3oaj/uPsZg/nvRUe4vj+J9fWsrtQYuORSoiGwCcB9CbvhvAJ40xH9303lMALgH4qDHmk/sedYBIRAqxPYkEM8rGx1k2Vl7O63iuRNWdO8CzzzLbEKCoe/rp3PigxGKMTNbX83tbW2XAEzpSKaYmDQ/7KwtKpwyOWIwXo+HhjaY8lZUsbO7uZnqyXF6FKC6syEsk/BpMe0N2nI3P00kXmvbndBzH35cuCiMRXxyWlGTu5l+MInL9l7sAfALAWwHMAfhvxpjf3OJ9PwXguwB8rzHmjX2POkAkIoXYGcZQUN28ySwzY5hxkovgjOuyPcjrr3NOX1kJPPMMy6NyIeqiUeqHhgaKyZYWicnQYQzF5NAQC3itI6EIjuVl35nLrjIBPDnb2pj2evAgbZhzmZMvhBD3o1hFZBiRiBRi9ySTwOwsAwKLi5yf1ddnf562sEDDnVu3+PPBg6yX7O7OjahbW+O8tbGRrUgkJkOIMRQsw8PA/LxfwyOCwwr8iQmuct29uzH1rKSEBc5WVLa3K/VVCBEMEpHFg0SkEPtjZYVZZZOTFJdNTdn1ITGGwaBz53yvjbo64MQJCrtcBIesmFRkMuTcu0cXqLt3fTGpAyF4kknmwd+8yRWn2dmNKWylpYxOHjjATUXPQohcIRFZPEhECpEZUim/36QVk9mMTMZiwNWr3JaXuc9xgN5eOqv29GR/Xri2RhFdV6eayVCzuEgxOT3t27nnomhX7IxYzBeVN29uTH0FWOidLipbWiQqhRDZQSKyeJCIFCKzuK7fIsTzst8ixBh+39WrzGazWWw1NYxOnjiR/ehkNEohW1dHo0gFNkLKygqjXjdu8ICQoMxP1tYYobx9mxePxcWNr1dUUFS2tbFJbXMz8/W1QiSE2C8SkcWDRKQQ2SGZpCv/6CiFXVNT9suQ1tYoXgcH/XmhjU6eOcN5YTaxYrKmhqm1bW0qvQotq6usmZyaoiuT4+TOiUrsDpuTf/MmH5eW3vyesjJexKyotJv+P4UQu0EisniQiBQiuyQSFJMjI/w5F2LSGAYaBgdp/mPbynV2Ukz29mY3qGD7TJaWsh96V5e6QoSaaJTGL9ev0x1qu0bUIniWlykm5+f9bXV16/dWVvptX2pq2L8u/bGmhu9RFFMIAUhEFhMSkULkhnic6aYTExR5dp6VbaJR4NIl4PJl34inuZmurgMD2RW0rsuIaCrFqGR/P79bqa4hJhZjyuvkJFcaSkqYKplNNyqxf2KxjaLSbsnkg3+3pISLBi0t3Jqb+VhTI3EpRNgodhHpOM47ASwZY85ncnD5iESkELklHufca3KS/ha5KhtLJBiZvHDBDyrU1gKPPMK6yWw7yq6uMt22spLRyc5OBaJCz8oKnV0nJylSysspNpQDXRgYw5N6bc0/wVdX/efWecuuXm2momKjqLSbVpmEKF5CICJTAD5jjPlXmRxcPiIRKURwRKN+UGZ5OTdBmVSKLf7eeIOZhQDncg89xC3bwi6ZZADK85jm2ttL8yEFJEKMMQxZ37pFU55UirV2NTUSFMWA6/JiMzfHFbS5OW6x2JvfW1nJi0JvLy2my8tzP14hRPYIgYicBvAHxpifzOTg8hGJSCHyg81BmYoK9mHMFsbwu86fZ2cGgDWMJ08yOpltR1djKJxjMWqFw4eBjg5lNYaeVIoh+qkpYGaGB4o15VHouniwUcx0UXn37kZTn5ISrjT19XGrqwtuvEKIzBACEfnfAPQaY96aycHlIxKRQuQXNigzMsI5VXNz9lNN79wBXn+dvicA525Hj7JusrExe99ticUoKK2TbHe35osCFJTLy4xi3b7NE8MYRqeqqxWlKjaM4f/15CS36WnuszQ3U0wePEiHsqoqpTAIUWiEQEQeBfAigN8E8HFjzA4qxwsTiUgh8hMr7i5d4jwpFymfs7OMTI6N+QGgw4fp6Nramt3vBqgZlpaY8trUxO9uaVGJnFgnkaConJ3lyRGNcr+cXouTWIwR6akprnAlEhtft+kajY3c7PP6er85rzH8nNVVHi+bH+NxHjuVlRSlVVVvfl5RIbEqRKYIgYj8PQBHALwNwDSANwDcAbD5A4wx5vv2PtzgkYgUIr+JxYBr19jCraEhN3PlhQXWTA4P++1BbK/Jrq7sfz/g+3OUlVFMtrYyo1ElcuJ/Eo0y9XV0lPnglZUUlJrwFx+pFKPRk5NMc15YuL9hj20hk0rxGPG8/X13JOILy8pKRsE3C067T46zQmxPCETkTq84xhhT0GvkEpFCFAYzM8DFi7z+NjfnZp6yskI316tX+b0AXVXPnqXnRS7GYNuEeB6DC52drJ1saFA2o1gnPQ3y9m1O+hsb/WiUKD5shHFhgdviov98aWljGmxFBUXeVltFBcVoNMrPi0b9LRbjdj+xuhX19bSfPnyYfY2CFpSxGJsFLy1RWNuttlbnhwiGEIjIvp1+oTFmcqfvzUckIoUoHJJJRgcnJjgPyEWfSYDzkEuXuNn5VEsLI5OHD+cuOui6jE4mEpwjNjb65VG1tcHP10QeEI3S5XV8nCdMXR0jRCI8uC5XwEpK+H+/X7FkI5qbRebmbXl5o+Csq+MF8tAhoL09dxeoRII3idFRuh1vFYl1HN5A6uu51db6jzU13CQyRTYodhEZJiQihSg87t1jVHJtjQIqV/f6RIJRyQsX+N0AI4JnztCIJ9e1i7a0yfMYlezq4lytvl5RytCTSjF8PzrKKExpqR950mqDyAaeRzOgsTEuYtiGvADF2eHD3LIhKF2XkfjRUdaQ2jqESIQrbe3tHM/yMs8He+HcjspKisnaWp47VlxWV/srh46zcbPYQv6Kisz+O0XhIxFZPEhEClGYuC7nCqOjfG7v87n67qEh1k1aR/6aGrq5njgRTJsOG6W0gYDGRuDAAT9KqVrKkGIMD9KZGb+FhDFc8aiu5kRZolJkGmMoKEdH3ywoq6q40mUFmhVn9nm6SEv/vFSKF7pk0n9cXaVonZzkzwCP564uYGCAonWrQvpUir+7tMSo7dKSLy5XV7lKaIXoXqmoAD70odxYfIvCISwi0nGcbwbwXQBOAqgxxhxZ338SwDcD+ENjzM39DTlYJCKFKGxcl2aVY2MsCyorY3QwF6LJ8/i9r7/Odm8A5ysPPQScPh2sYWZ6lLKsjLWU7e3822hxPMQkk5w0Ly5ykn/vHifokYhvkCJRKTKJFZRjY9zSBeVWRCI8DktKeLxa0figOWx7uy8c99vo1xj/Irp5W1vzx2KMv9mfAf7u0hIL+L/1W5UaK3yKXUQ6juMA+H0A372+KwqgyproOI7TCeAGgJ81xvzSfgcdJBKRQhQPS0ssgbl+neKpvj43Qs4YLoSfP8+5EsD5z8AAcPIkjXCCnJe7Luc9sRjH0drKdnPNzWofEnpSqY2icm6O+9U2RGQDY3i8ra7yMf15ukjbipISirGyMv+xvJzpqocPc4UsX0gkgD/7MxodHT8OvOtdWpwRJAQi8ocBfArA7wH4SQA/AeDfpzuxOo7zlfXPfOe+Rx0gEpFCFB+JBDP37KJ3rjofGENzzPPnKWbtJbelhWmuR48GHwU0xp+nlZVRTB44wCwyIRCLMbQ/OclVmZISrsYEkaMtwold9bJpFFYwFlpO/vw8haTrUkSeOBH0iEQ+EAIR+RoAB8BbjDHGcZyfB/Bzm0Tk7wD4emNMTwbGHRgSkUIUL7bzwcQEe7OXl3OxOhcLwktLNOG5ds3vCV9aChw5Apw6Rff7oLHtQ1IpRiUPHeKjMq8EAEaH7t6loIzFeALV1Sl8LcROGRoCvvxlnjMf+hDTQES4KVARuZtpwXEAnzHbq867APJgGiSEEFvjODSWaWqiKd/oKCOFuRCT9fXAU08Bjz9OEXv1KnDzJjA4yK2tjWLyyJHgRFtpKaOkAKOTr77KuY6NTqptSMipreV26BBXG27dYog9laJZiCyAhdieY8e4gnn1KvDFL7I+Muh0FCH2wG6mKS6ABxVDHASwsvfhCCFE7qirYyuOgQGaBd68mRsxaWsjBwYYFbXRyZkZ4CtfAV54gWLy1Kn9e0HsB2uQaF1vx8b4c28vBa/SXUOMbVfQ2MhJ8d27XAlZXMxtjx0hCpFnnuE5MzfHi/7736/VOVFw7Cad9RyABgCnt0pndRynEsAwgEFjzPuzNeBcoHRWIcLJygrF5I0bLLdpbMzdfd11KdIuX+bcAmC5z+HDwCOP5EeqK8B2IcvLLE2qq2OEsrVVvesFeBDfuAEMDzNvvLFRaa5C3I/FRdZHxuPAW9/KC70IJyFIZ/1/AHwawK87jvNvNn1BCYBfA3AAwM/sdaBCCBEktbXAww9TuE1MMPpWWsrIZLbnwqWlDOgcPUpDzEuXKCpHRrh1dnJs/f3BeklUVPiZV7EYcOUKBWVjo+/uKgPPkFJaygO0q4s1k2Njfn8dRVmE2EhDA811vvAF4MUX2Y6kszPoUQmxY3YTiSwB8HkAHwBwG8AygKMA/gzA06CA/Jwx5luzM9TcoUikEAJgTeD165wPG8N7fi5LvpaXGZkcHORiNcDo30MP0SE+n8poolFGco3hPGhggDWgIsSsrVFIXr/OlQUdEEK8mRdeAN54g/UB3/7tSusIIwUaidyxiFz/oFIAHwXwwwBa0l5aANt/fMIY4+5jrHmBRKQQIp1EgtHBkRFG32ytYC6/f2iI0cnFRe4rKQG6uxn46evLn3mHMXShjcclJsU6y8ss+r17l6sg1dVBj0iI/CGVAv7yL2m2090NfMM3FF7rErE/wiAi0z7QAXAMFJKLYB1kal+jzCMkIoUQW+F59EEYHQXu3WNUsr4+d/d7z2OK7aVLNMW0l2/HoWDr76dpZl1dbsazHelisqODjrMSkyFnfp6h9ZUVWgCrXlIIsrICfPazFBKPPUYLbxEewiQiix2JSCHEg1haYprrjRucCzc05NaQcm2N3z8xQVfZVNoyXkuLLyibm4MtR7NiMhaj0JWYDDmpFA/coSHfClkIwZvJX/0Vn7/3vUzjEOGg2EWk4zi/D+CLAL5sjLmZueHlHxKRQoidEosxKjg6yvtAQ0PuaxUTCUYoJyZYfpZI+K/V1bElR18f/U6C6rxgDLMao1E/zVX6IcSsrtKVaWaGLUHUX1IINua188/eXrq2NjYGOyaRfUIgIj0A9s3DAP4ewJdAUTmfiYHmCxKRQojd4rqsmxwaorCsrQ2m9Mt1KWonJhjwWVvzXystBQ4epKDs6QmmB6UVk7EY50aHDrFFiNoKhhBjWAd2+TJztYMOmwsRNMawXuGVV7gaGInQSe0tb8kvJzWRWUIgIk8C+DoA7wPwLgCNoKg0AC7AF5XPGmNWMzDuwJCIFELsFVs3OTxME5zKSkYDg5gbG8NAz9QUBeXs7MbXW1spKHt72Ycy12NcW2MpUEkJRe3Bg0p1DSXxOE+YqSkeAPniEiVEUESjwMsv05rbGN5InngCOHFCpjvFSLGLyE0f6AB4DMB717dnANirftIYU9BdwiQihRD7xRiKyPFxBltsu7wg7/8rK37Lkps3ed+yNDWxbcjRo7mPoKZSrJtMJqkhbHRSGY4hY36eUZi1tdz30xEiH5mdBc6dY3oJwIL3Z54BDhwIdlwis4RJRG768EcAfBOAHwPQDsAYYwrack0iUgiRSVZWGGS5fp3isq6OC8tB4rrA7dsUlOPjftprJMLI5IkTjA7mWvTGYkx3dRy63R88qF71oSKVosHI2BgPhqoq5l3rABBhxRhepF94gRdHADh8GHjqKaVuFAthEZGO4xyCH4H8OgCtABwAE2A665eMMX+8nwEHjUSkECIbJBJslTc2Rl+RXLcIuR+pFAXu4CAfPY/7a2qAY8e45drbwfMYnUwkWArU08NWIUGlBosc43nsozMxwZMmEuFBqOJZEVZcF7hwAXj9dT4vKWH6yKOPSkwWOsUuIh3H+W1QOPaBonEarIP8e1A4TmRktHmARKQQIpvYVNfr1/1+j/kQnQQoboeHKSgXF/39XV0Uk/39uR+n61JQui6/u6+PNZwKUIWEtTXmhI+Pc1WhujoYVygh8oGVFeCll4CREd48IhHaXZ85QwKd+vMAACAASURBVHMqUXiEQERad9a/A/DzxpgXMzfE/EIiUgiRK/I1OmmNM69d89uXABxXVxfrFvv7Ga3MJYkEM7pSKX63NQXK9ThEAKRSrJscH6d7VWkpc51LCrqCRoi9sbAAnD/PVT+bPtLXB5w9y7QNUTiEQEQ+C+BJAOUAkgBewnr6KoAXjDHJfQ6yG8DHAXwQQAuA2wD+HMDHjDH3dvlZ7wDw46DhTzOAeQAXAfyGMeavHvT7EpFCiFyTHp28eZMRtqam/JgfJxIUuaOjjJza+YrjcK5y6BC3urrcjisep6D0PH53dzcNeWpqFKEselZXWTs5McH/7IYGpbqKcLK8zDTXq1e50ALQeOfsWRaV62KY/xS7iFz/oCoA7wDbfLwHwFkAEQBrAJ4DBeXfG2Ne3eUABwB8DTTm+RyAQVCwvgfANQBvM8bM7fCzPgrgEwBmAfwlKEZb18f6ZWPMv33QZ0hECiGCJBrl/Hh8nOKyoYHurvlALOab8dy44c9ZAEYEDx1imU6uHV5jMWZ5eR6/u6eHglI1lEVOPM5VF5val08nixC5JBoFLl5k39VEgvva2oDTp7nCpnSN/CUMInKLD24Ehd7XAfgnANpAd9ZdLQc6jvO3AD4A4EeNMZ9K2/9rAH4CwGeMMR/ewef8EwB/AuCLAL7NGLO86fWynURMJSKFEPlAIkEH1eFhtr9oaMivftOJBF1nJyb4mFy/upaWct5y5kwwdZ7pKa+VlZw/tbdTUAadJiyyRDLJ/OvhYQrL+vr8KDIWItfE48CVKxSU0ai/v6mJkcmDB1mTkE83k7ATNhHpOE4TKCDfBxruHF1/aVctPhzHOQxgFHR3HTDGeGmv1YGRRAdAuzFmdZvPiQAYAdABoN8YM7Orf1AaEpFCiHzCdVk3OTTE+0xtbe4jfQ/CdRmZHBxkpBJgfefDD3MLar6STFJQui7Hc+gQ50/qZ1+kpFI8WYaHmfKajyeLELnAdXnTmJzkamQyLYYSiTBKaUVle7vSwYOk2EVkWiqrbe9xBhR3DoAlAM/Cb/FxaReD+34Avw3gt4wx/3KL122U8n3GmC9t8zlvB/BVAP8dwHcC+HoADwGIAXjJGHNup2OSiBRC5COeRz+R4WHWT5aXc46cb/f+u3eBV15hfSdAAfnoo8BDDwWbaei6/LulUkBnJz0ompqU7lqUbD5ZIhGm82n1QISRVAqYmeFK382bvEh7nv96aSkviCdOsJ5SKRu5pUBF5G6mHvcAlIGiMQbgH7BeAwng5fQI4i45vv44dJ/Xh0EReWz9++7HE+uP0wBeA/Bw+ovrxkD/eD8RSiGECBK7eNzaynnx7ducDySTFJR1dflhxNPeDnzjNzK78OWXacbz0kvMrjpzBjh1KhjhW1oKtLSwdG5hgeOrrmbf7o4O/g1FkWBPlrY2RiTv3ePJMjPDA6C6mpsmyyIMlJRw5ayzE3j8cb9W4uZNbvPzdE4bHQ22QbAoKHZzG38drDX8ewBfM8bEMzSGhvXHxfu8bvc/6EhuX3/8MIBxMM32RbCv5a+Ckck/BfDurX7ZcZwfBPCDANDb27uDYQshRDA4Du/tjY00sbGCyBrdVFTkR/1fZyfwj/4R5yivvAJMTwPnztFI8MwZzlGCEG6Ow5K5+nqWD12+zBKinh7WT6pvd5FRU8Otu5v/4QsLvqD0vPwN6QuRLcrLGXns6+PPy8tMfR0aYlPe11/n1tnJm8zhw1plE29iX8Y6GRmA4/wWgB8A8APGmN/Z4vVPAvgIgI8YY35xm8/5ZQA/DcAD8BZjzBtpr1WBkc5uAM88KLVV6axCiEIkleL8+NYtLjJbY5na2uAFpTE033nlFWB2lvvKyjg3OXmS0csg00o9j3OnRIIisq+PEV95sxQxNr95eponjQ3pS1CKsJLeIHhsbKNb2qFDXPnr7NT5kWlCkM66+UPrwSjiojFmaa+fAz/S2HCf1+s3ve9+2F6SY+kCEgCMMdH12srvA1uH7Lg+UgghCoWSEqZrtrRQmFlBeecOBWWQ82PHoTDr7aWb68WLFLrXrnFrbmY5ztGjwQi3SMTP3IrFGJ30PArJ3l6OT50jigyb39zSwoNvcZEni80RLytjSF8TZhEWHIfOY11dwNveRiE5NMQbyfAwN5sa293N+snW1uBXKUUg7LYVRwkY7ft+AIfS9o8D+B0Av2KMcXc5hmvrj8fu87p1fb1fzeTmz1m4z+tWZKqqXghR9JSW8t7e2sp2G0tLfsAlHuc8oLY2966pjsMF7UOHKHIHBzlHmZ8HvvY14MUX+Zr1dwgiOllZyc0YYG0NeO01zpHsvKmxMT9qT0UGiUTostTUxPQ9Kyhv3WJ4WoJShI2yMp4Lx4/zBjI0xBXAuTm/lhLgTeTAAW7d3exFJbeyULAbd9ZyAH8D4F0ADIAbYPuNLjBN1AHdUT9gjEnseACOMwC25pjA/Vt8RAC0PaDFR+v6e1fBdiCJTa//NYAPAvhOY8wfbzcmpbMKIYoVY1j+MjvLOcDKCu/3dXXBpW6mUnShHxxkXae9LTU0MHvq8OHg/R08j3+rWIw6orubzviqnyxybJ7z9DQPzmSSB2h5OZ1ey8s1YRbhIhrlzePWLT4ubUpGrKnxRWVnp0TlTijQdNbdiMifAfBJAH8J4CeNMcNprw2A5jXfDOBnt6tdvM9n2zYeP2qM+VTa/l8D8BMAPmOM+fD6vjIAAwCSxpjRTZ/zBwC+C8D/YYz5aNr+9wP4W7AVSb8x5n7RSgASkUKI8LC2xijg5CTnAiUlvOcHFXBZXmZ66+AgTTUtra3AwAC3urpgxmZxXY4zmaSIPHyYJqAKUhU5nscTZnWVJ838PFcW7DyqooIrMTIgEWFiaYmC8sYNPkajG1+vqaGYPHCAabKNjRKVmwmBiLyw/vTMVu08HMeJADi//pkPb379AZ89AOBroMPq5wBcBfAUgPeAaazPGGPm1t/bD7qvThpj+jd9TjuA5wEcAaOiL4HurN8KRk//qTHmTx80HolIIUQYWV5mBt/kJAVSVRVTXoO433seF7lHRphBlUjLLenooJg8dIjjC5JolH+3khKgv5/zpKDHJHKI61JYrqywjcjs7MZJtOMwLbC8nI9aaRDFjDFcXLl9m9utW0zfSKeqyq+7PHhQohIIhYhcA/ApY8z/ts17fgnAjxhjqvcwyB4AHwdTTlvA1NQ/B/AxY8x82vv6cR8Ruf56M4CPgsLxIIBlAM8B+A/GmBd2MhaJSCFEmEmlOB+emmJPasehMAoq3dV1ucg9MkKB665X3jsOF7gPH+ZWves7T2bHuLTEx5YWCtyWFvlNhJJkkhPneJyPy8vcVlY2roY4DqOXVVUSl6I4sU15rWX47dtcdEnHtt85eJBbkBfyoAiBiFwA8IfGmB/e5j2fBvA9xpj7Oa0WBBKRQghBYjG20xsfZxZfJBKsoEwmKW5HR/mYSnG/4zAKeOQII4JBtuZYWeE8qbycYrKrizpBCLguxWU8zknj7CyNSqy4jER48FZVaQVCFB/G0LTKRilv3nxz+mtLiy8ou7rCYYsdAhH5LIDjAB4yxsxs8XorgEsAhowx79zHeANHIlIIITZiDMWRNeazXgrV1VxIDiIbKZFgZHJ0lJFKKygjES5sDwywrUiuHWgtyST/TqkU+2D299P8U9pAvIl4nKs0S0sUlvPzzOkGeADnQ7NXITKNTX+9eZMX8du3/VQTgHUC1mbcbk1NxWePHQIR+R0A/hjAJID/HcCXwZTTTgDvBlNI+0H30z/Z96gDRCJSCCG2JxplltLt24xUel6wfShjMdZOjo5ygdvOv0tKgJ4eX1AGsahtBXg0yiDToUOs61R0UtwXY3jArK76zV6N4QmmA0cUK67LGgorKmdmfOMqS0kJ6yjThWVLS2FHLItdRK5/0CcB/AxoUvOmlwH8sjHmZ/Y0yjxCIlIIIXaO6zJDaXqaojKRoGtpUCml0SjTb0dHOR57mysvZ8uQ06eDaxmSHp3s6KCwVXRSPJBEghHKiQmebCUlPMkKeeIsxIOIxXjcp29LS28Wlo7jp8Vst+Vr7XEYROT6hz0N4PsAnAXQAGARwOsAfs8Yc26fY80LJCKFEGJveB7v81evsi6wsTHYjgerqxSUIyMUuZbeXuChh5j2GkQq7uboZH8/F9SDcsMVBcTqKiOTU1OcZFdUsO+NViJEGEgkmAKbLiwXFvx6hu2orORFtraWotI+t1t1dTDnUVhEZBiQiBRCiP3heZznDg7ynt/UFPwi8NwccOkSMDzszzcaGigmjx0LTuwmEjTvTKU4x+npYd/J+noJSrENnseopO3Plx5yt/0qd3oAuS4PxHh8o/1xSYm/lZb6j0LkE6kURdjKChdZ0jfrdLa66tc53I9IxI9oWoOr+20VFZkTnMUsIh3H6QXwBJjG+rIx5npmh5hfSEQKIURmcF2WtwwN8f6dD54IsRjF7eXLnF8AnG8fP85U14YA/cXT013LyxkpbW/nmBRoEvfF9qtcW2N/nvl5Htx2jldWxklxSYkvFlMpvu44PNgaG7lyUVfHfbZVSXq7kliM+7eaOzrORqGZLkDzDdvg1QplK7wlkIsXY3wxubLiC0773IrNnWJ7wG63lZfzmLKLOukLPPZ5SYkvgotNRDqO8ysAfhyseQQoJH/dGPPTGR1lHiERKYQQmSWRAK5fZ1qp4+RHHaDnscTs0iXWTgJ+qxDbezLIViGuy3luMsn5yIEDdLyXoBQ7wk5M19aY7jc/TxFYX0/BaE16Kit3V1tpDA9O1+XBaZ8nEvw+KzqjUT4mk5woNzQEL9JiMa7S1NZy1chxeJItLvJvFI/7/8bSUk70Kyr4XGkBxU/6Ykw0unGLxd78cyYoKfFF56/+amY+M4PsWUQ6jvNPAfwBKBwHQSF5fP3l7zbG/FGGx5oXSEQKIUR2sC6q4+MUQg0N+eENMjtLMTkysrFVyIEDdHYNuvek6/q96isqaMjT0cG5sBB5zdoa023Hx3kg29qzXGLFY00NxWNb29YrMTatMBajuFxY4GMs9uboayTCi1dpKR8dZ6PQ3Oq5hGjx4HlcINluSyT4GI/7GQCbn6dS/KyKCuA3fzPof9Wb2I+I/BKAdwD4emPMl9f3vQ/AXwP4ijHmfVkYb+BIRAohRHZZW2PN5Pg476dVVflhKhOPU+SOjbHULL1VyMGDwfeeBDamvNbXczxtbcGOSYgH4rpcrRkbY+SvvJwHcDbD6lY8VldTPLa37+37jNkoCmzU1bZhiUa5377Xzq03P3ddpmHoZBUW1/XTar/lW4IezZvYj4icAfBlY8x3bNr/3wG82xjTmtGR5gkSkUIIkRs8jyVck5N0T41EOK8M0tHVYqOmIyMbfUts78nTpyksgxS+NmBiDOfHvb35YWIkxLYsLXGVxq7U1NdnVljF4xSqlZXAyZN7F4+Z5s4d4LXXmEYghKVAjXUedJtpAnBti/2DAD6034EJIYQIN5EI+0S3tPAeevcuAxULC5xTBulQWlkJnDjBbXPvyYkJbk1NwMMPA0ePBiPcKiu52ZYhr7xCkdvXR4GrdFeRl9TXA6dOAUeO+Cf90tLG96TXipWWbjzBUqk312SmB0UqKoAzZyge88nYp6ODF7uVFZ2couB50C0vAiC5xf4kfKMdIYQQYt9UVVH89PZSRF6/TsFmDF+rqQlOUFZVcc576hQzj4aG6O567x7w7LPAiy8y4HH6dDBzQ8fxW525LlsIjo1R5B4+zHlrPs2lhQDgWxB3d/tponazDqq2VUMi4f9eWRlPyvp6vyWDdbxMd6LNNxyHq1LPPx/sBU2IDLCTdVM1khRCCJEzrHtrUxPnW/PzbBNy9y5fq67mFtT8q6YGOHsWeOQRRicvXuTYzp8HLlwADh1idDKojLXSUqC5mc9XV4FXX+W+/n4aBdXUBDMuIbbFRh3vd4Da6KNtH1KoNDRwtezWLf9EFaIAeVBNpIfdi0hjjCnoagzVRAohRP4Rj1NQ3rgBzM1xX01N7o0et2J6mmJyfNw34+noAB56iOIt6BpF12WJWCrFqOShQ5y/FvJcXIiCJRZjCkM+tD0RwVOkNZHA7tNWFZsXQgiRcSoq2Cexq4uCcm6OKa9377K2srY2uDYcHR3cVlaY5nr1KoXl9DTHffQoo6otLcGMr7TU/25bO1leTjHZ2ZkfQlyI0FBZSbfYK1dYtylEAbJtJDKsKBIphBCFQyzGzgETEyyhKilhqVSQ/SeTSWB4GBgcBGZm/P1tbRSTR44E70Druqw99TzOY/v7mUKcDyaWQhQ9qRTw3HNc4QmyCa0IngKNREpEboFEpBBCFCYrK4xMTk5SXJaXA3V1waZtzs5STI6MMIIKcN44MEBB2dERrL+GdXaNRjmXtdFJzWuFyDIzM8BLL/GEE+FFIrJ4kIgUQojCxhjWAN6+zZTXVIpmjrW1wQk212XN5OAgPTUsjY3AsWOMTtbVBTM2SyLBTgvGMG24p4dlW6qdFCILGMPc8pUVpk+IcCIRWTxIRAohRPGQSrEVx/Xr7PVdWhq8MFpcBK5dY6uQ1VV/f2cn6ycPHw42EmgMU4NjMf6dWlqY8trYSDMjpbwKkSGWl5nW2tqqEyusSEQWDxKRQghRnKyu0t11YoJCqaEh2NpEz6O4HR5mCq7rcn9JCaOAR46wG0CQBo6ex/lNNMq/WSTC2s6ODgZP1O5OiH0yOMjmrkE5b4lgKVARKV9hIYQQoaGmhqaIhw4x1XVsjOYydXVMd801kQhFYl8fU0knJigob97k84kJ30X1yBH2ecx1sCIS4d/Ntu9LpZjyOj1NUVlaytTX7m6KcglKIXbJoUNcTUomg3UEE2IXKBK5BYpECiFEOEilaHwzMsIU06oqCsqghdDqKgXu8PBGd9eaGhryHDnC7Legxwnwb7i8TBFcU8NU3Pb24N1nhSgobtwALlxgiF+EiwKNREpEboFEpBBChAtrxDM+zrpJx2HvxOrq4IXawgLF5MgII4CWhgaKySNHWKuYD8RiHKPjMB23u5spr0H/DYXIezwPeP55nixBpEWI4JCILB4kIoUQIrzEYjTiuXEDmJvjvqqq4Gv/jGH7kpERYHSUcw5LezvF5MAAhW/QeB7FZCLByO7hw6yjVKaeENswNwe8+KKikWFDIrJ4kIgUQggBUATdu8caxbt3KeSsoAzSSNHz2CZkZITR00SC+x2H0b+TJ4He3vxozRGLMd3VcTg37uhgFLWqShFKId7Ea6/xopMv6QUi+0hEFg8SkUIIITaTTDK19PZtprx6HlM1KyqCHZfr0tl1ZMTviQkwInn8OHDiRH60oPM8YG3Nd3mtrGRLk9bW/Pg7CpEXrK0xGul5EpJhQSKyeJCIFEIIsR2uy8jk0BDv/bW1+ZFGGouxfvLqVQYzLDY62deXH9FJgH/DlRWKc2OY9trVBTQ3c+6sKKUILfE4TXZmZ9U/MgxIRBYPEpFCCCF2guexjGloiDWA1dUUlEFjDFtwXL3K+kkbnayq8qOTDQ3BjnEzsRiDMK4LNDVxjArEiNDiebywjI6yf6QKiosXicjiQSJSCCHEbjCGqa6jo4xQVlTkT8/EWIyprleuvDk6eeoUo5P5FuhYWWGbk85O4Nix/BDmQgTCrVvAG2/kT7qDyDwSkcWDRKQQQoi9srTEGsUbN4DSUtb7lZYGPSo/Ojk4SFFpo5M1NYz6nTzJ5/nE4iIz+/r62I9dnQ9EKFlaAl59lSdtU1PQoxGZRiKyeJCIFEIIsV/W1mh0c/066/6qq4NvE2KJxZgpd+UKhRrAaGR/P8XkwYP5MU6AWX0LC5w/DwxQUJaXBz0qIXJMPA5cvAjMzKhOstiQiCweJCKFEEJkilSKaaRTU0x1dRxmplVWBj0yRidv3mTt5MQEBRvAVNxTp5hKmg/jBPy/YyTCus4DB/IjwitEzvA8phEMD6tOspiQiCweJCKFEEJkg1iMgYSJCdb9lZXRlTQfxNDKClNdBwdZjwhQsLW10TX1wAHWKAY9b3VdYH6eYtzWntbW8u9YWcl9FRX540IrRMa5cwc4f54HfF1d0KMR+0UisniQiBRCCJFNjAGWl9lzcmqKc4h8cXb1PNZ0XrlCTw8bnQR8UXngALeOjmBFpesCiQQ31+VYbRpuZSXrUTs7GbRRH0pRVCwvA5cvMzzf1BT86o7YOxKRxYNEpBBCiFyRSjGyNjHBtnCRCKNr+TAnTCQY9Lh1i4J3ZoYC2FJSQlHZ388U03xJfQU4L4vHWZtqDNDeDvT2cr6dD5FfIfaNMTw5r1zhz01N+VPMLHaORGTxIBEphBAiCNbWKNrGxyngqqoYncyXeWG6qLx1i6LXTiNKSmh8c+oUBVu+jBngGFdX+fctKaFx0IEDFOvyJxEFj3XKunGD4XfZGBcWEpHFg0SkEEKIIPE8ZqlNTrItRyTCuWG+uZLG4xSTg4N0obVTipYWismjR/MjoppOKsVMwESCf8/eXqblVlcrQikKnPl5OrhGo0BzswqDCwWJyOJBIlIIIUS+EItRSI6N8Xk+9Z5MZ2mJLq+DgxwnQJF29CgFZXNzsOPbCtfluF2XP1dUAI2N/Ptao57KSv478imyKsR9SaWYGz805BcGi/xGIrJ4kIgUQgiRbxhDwXP3LqN+8Xh+ubtaXJdz2MuXmfpq6exk7eTBg4xU5qMos3WU1qjHUlLCv3NbG6OW+WCAJMS2rKxwVWdmhg1qddDmLxKRxYNEpBBCiHzG8ygop6dZBmVTM2tr80tQzs1xHjs8zDFaqqspJru7+VhTE9wYd4Lncfyrqwz01NVRELe25peZkBAbMIYn4dgYH0tKWAicTxcJIRFZTEhECiGEKBQ8D1hcZNTv5k0gmfTbx+VLtC+RYCuTGze42T6UluZmX1AeOJD/c9xYjIEez2N0sreX/4Z8H7cIMaurLGCemKBoqanJ/9WbsCARWTxIRAohhChErCHP1BSjlABLovKpR6IxwMKCLyhv3dqYOlpRATz6KPDQQ/lnyrOZza6vtn9mQ4M8TUSekkr50cl797jyoQM2WCQiiweJSCGEEIVOLMb6ybExzk8qKhidzLeWFq7Lcd64wVrP2Vnur6oCzp4FTp4sjAif59H1NR7nfLy9HejqUscFkcesrDB9YXKS4rKpKf9XbooRicjiQSJSCCFEsZAe+bt1iz9b59F8wxjOaV95xY+k1tQAjz0GHDtWOMESz2N0Mhr1/95dXTQUqq/PPyEvQo7rArdv09E1kVBudq6RiCweJCKFEEIUI4kEo34TE4yalZfnp6gxhsGRV15h5h3AcT72GHDkSP6N90HE434NZSTCKGVHB4M+ZWWcr9utUISyKEJcl6s4Q0M8WBsbJSZzgURk8SARKYQQopgxhmY8169zzug4FGnl5UGPbCPGAKOjFJOLi9zX1AQ8/jjdUQtNTAKcm6+uUlhunoI5DreKCn9raaELbHV1MOMVISSZZOrCyIjEZC6QiCweJCKFEEKEhXiczq5jY6yjrK7Ov5Zynsc2Ia++yggqQAFZV0fxW19PbxD7mG+tTnaD57E8LZXi3DIW8+fxfX3MNMzHVGRRhCQSXGkaHeXPjY0KlWeDAhWRBXqJFUIIIUQmqKigOOnpAebnmep69y5FWH19foixSAQ4fpyprIODwMWLjEzabTOOQyHZ2AgcPswt36Ks9yMS4Wb9Taygj0aBCxf4vKXFbysiHxSRNcrLgYEB9t+ZmuJKE5CfaQsi5ygSuQWKRAohhAgzm1vKRSJ0GK2qyp8UUtdlVHJxEVha4mafLy9vTBUtLaWQPHqUvSjzpX/mXrBtRaJR/tzZyX9TdTUjlAoUiayxOW2hqoqrHIV8QuUDBRqJlIjcAolIIYQQgimVKysUZtPTjFSmUhSSlZUULvkiKtNJpSgk79yhR8jt2/5rtbV0ej16lJHKQsbWV0ajnMcbw/+Xujo/1dfWVkpgiozhebR8npjghaGkhAebwuJ7QyKyeJCIFEIIId5MKkXRsrgIzMywp2Mqlf9zyMVF1lQODfk1lQAdUo8fZ2podXVxBFRcl6VsiQT9UdIpL6egLCvzxaXdl+4OW1cnwSl2SDTKVZrxcR501dXsy1MMJ1OukIgsHiQihRBCiAdjI2Fzc/TesHPIfDPmsRjjt8MbG9sossrKfJOe9K2hgXPifIy47hbX3WjcYzfP89N/jaEb7KOPquxN7ALP84uqZ2Z4wtTWygVqJ0hEFg8SkUIIIcTu8DyKyfFxPpaWUoDla0QrmaSQHB5mRDUev/97bXSup4epsC0txR1omZ9nudtb3qLWImIPRKMUklNTDP3ne6pC0EhE7g/HcboBfBzABwG0ALgN4M8BfMwYc2+HnzEBoO8+L08bYzp38jkSkUIIIcTeWVlh/8nJSUa66uvzPyARi/kGPZu31dWN721uZk3lkSP5G3XdL0tLXBh44gn+/wmxJ5aXafc8NcWTrLxc+dKbkYjcO47jDAD4GoB2AJ8DMAjgSQDvAXANwNv+//buPDyuu773+PsrjXbJ8hIv8r7bWUgaMIEsgEMgBGgo0EKXC21oA80tLaUXHtqnt5SELhe6UFpaChRKCnQFClxogHBDIM3CEkgCZHW8yJu8SLZs7dv87h/fc3LG4xl7RhppjqTP63nOc6yZMzNnfkeS56Pvbwkh9JTwPPuAhcAHC9zdH0L4i1LORyFSRERk6sbGvCCxZ49/lqyr866hs62b5Pi4Vyt37/b114eH/XYzWLnSA+WGDbPvfZ3P4KBvz3mOd3EVmbQQfHByVxccPOg/VJrd1SlETp6ZfR24HnhbCOFDObd/APgd4KMhhFtKeJ59ACGE9VM5H4VIERGRyok/Px475p8hBwf9c2M8w+tsKkpMTPj6608/7cO/Jib89kwG1q/3QLlyZTrW16yEkRG/dpde6kuJiEzZjd/2vgAAIABJREFUxAScPOk/SEeP+m3xVMLzkULk5JjZRmA3sA/YFELI5tzXhndrNWBZCGGg4JMkx+8DhUgREZE0GxryYHLkiAfLbNbn4WhpSX+311wjI8m4ytxlRGproaPD12hfvdq7v87mYktcid2+3dfbnM3vRVJmeDjprjA46OX8BQvmxkxWpZqlITINfyd7cbS/MzdAAoQQ+szsPrxK+XzgrhKer8HM3gCsBQaAHwH3hBAmKnjOIiIiMklNTb6tWOEBsq/PCxOHD3uorK31NRzTXqFsaIALL/Tt9GkPk52d/pn44EHfwMNxHChXrfL3PptkMrBsGTz5pH/m3749/ddGZonGRp+xavVq/8vS4cNeoQzBu7rOth+WeSQNIXJbtH+qyP278BC5ldJC5Arg03m37TWzN4UQvj25UxQREZHpUFPjs7i2t3t30OFhD2J793rFa9Gi2RFYFizwsYPPeY4XFQ4dSoLkwIAHsCef9GMvuMC35uZkWb2mpmSfxvdbU+NB8sABv0aXXqrJNqWCzPwvRwsXep/w7m7vL37sWNL3valp7vQTnwPScCXao/2pIvfHty8s4bk+Cfw38CjQB2wEfhN4C/BVM7syhPBIoQea2Vui41i7dm1pZy4iIiIV1dgI27bBunUeWPbsST5fpjFcFdLU5DO3bt7sBZUTJ5JA2dXln4+7u8/9+Hi9zbVrvQtpGrr5msHSpf5+vvc9D8xpOC+ZY+rqvD94R4f/Beb0aS/vHz/ui9GaebfX5mb9JaOK0hAizyfueX/ewZshhNvybvoJcIuZ9QPvAG4FXlPksR8DPgY+JnKyJysiIiJT19joBYm1a311gNkYJsHPeckS3y67zIc/HTnin4vj2U8HB/2z8tCQ/3toyLeeHq/K3n+/t8PWrd7zr9rvf/Fi6O2FBx7wJUDm6jInkgItLb51dPhfZIaGfA2h7m6flKe314+rq/O/vszXyXmqIA0hMq40the5f0HecZPxETxEvnAKzyEiIiIzrKHhzDC5d6/f3t4+O3u2ZTI+/KuYbDYJkSdO+DjLQ4f8fe/d6+F60yYPlEuXVm+Sm4UL/bN8HCQXltJfTGQqzJI+4MuWwUUXed/qvj7/YYmrlbnHNjbOr0l6ZlAafv1GIwTYWuT+LdG+2JjJUhyL9i1TeA4RERGpktwwefCgV+hGR888pq7Ot/r62dvLLZ6ltqXFx01u3eph7emnPVCeOAGPPupbPHxswwYfkznTFcrWVv8M/8AD3rV12bKZfX0RGht9W7rU+8GPjfkPzKlTHihPnvS/zMTHalxlxaRhiY9NwNOce4mPGmDp+Zb4OMdrvAz4GvB4COGi8x2vJT5ERETSb3zcl9mIt74+//zY1+eVvFhb29wYuxeCd3Hdtcu33Pdo5u+zrc2rtAsW+L6tzf89naF6dNQ/qz/rWd7dViQ1slnvK97X511ge3r8lwX4D01Dg/9yqOZfnbTEx+SEEHab2Z34DKxvBT6Uc/dtePXwo3GANLM6YBMwFkLYHR9oZhcDXSGEE7nPb2brgL+NvvzMtL0RERERmVGZjG8tUT+jjo7kvmzWPyv29CSTPNbVVadiVylmycyuz3ueV2SfesrfW3+/j7M8fdq7v+aL1+BsaPB2aGjwim3+162t5XeTra/3MZ8/+pFXJjdv1lqSkhI1NclfV1au9NtGR5OByCdO+C+JeGwl+A9Dc7MqludR9UokPFONvB9YBnwJeBx4HnAt3o31qhBCT3TsemAv0BlCWJ/zHLcCvwfcHd3fh4fNVwKNwB3Aa0IIeZ1fzqZKpIiIyNwRgoereAm68XEPSy1zaJDL+PiZQfLUqWTf3w8TZayWvX07vOAF5Q8ly2a9B+Hatb525mwN6zIPjY0lwbKnx7+RR0aSmWBbWqYvVKoSOXlRNXIH8F7gBuAVeDfWvwFuy68uFnE3vubk5cCVeAWzF7gXXzfy0yENiVlERERmlFmyFmW8BF1np1fwamunv7vnTMhkkmX28sU9+uJuv6OjyZb/9f798MQT/pn2JS8p73Nz7lqSIyNaS1Jmkbq65JfEypXJTLBxN9hjx7zMbubHtrTM+2/uVFQi00aVSBERkblvYMCX29i/PxkmFTPzABVP1lNXNz+6aB45Al/7mrfH8uVwww2TG0/a0+M9CC+/fG6MRxV5JlT29PjyIsPDHjZraqY2tnKWViIVIgtQiBQREZlfJibOrtD19yc93AYHvaKXyXjlci4Plzp5Eu64w9//woXwild4ICzXqVPeS3DNGt8m8xwiqTUykvyCOHnSx1fmznZVV5fMHnsuCpFzh0KkiIiI5ArBQ1VcuRwd9eJDW9vcXIauv98rkj093nPv5S/3yXPKlc0mYXLxYti40Z9nLraZCGNjHggHB5Ngefq0/7JYsKBwdwaFyLlDIVJERESKyWZ9MsdDh3yynmw2WdtxLhkZgTvv9PdYXw8ve1kyweVkDAz41tjoYXLFCv9sLTKnnTrlU0QfPpwMXs79K4pC5NyhECkiIiKlGBvzal1npxcdamt9dYDGxrkxhnJ8HO6+G/bs8fd27bWwadPUnnNszD9Xg3dzXb3aizQic9rAgP+i6Oz0ELlwoYfKWRoi53CPfhEREZHpVVfnFbUVK7wH2/Hj0NXlEzqGkKxjOVsrbpkMXHcdNDXBo4/CXXf5591LLpn8c9bV+VqX2ay3VWendwtet85vb2qq3PmLpEZLC1x0kZfhDxyAvXv9l0Rra7XPbFIUIkVEREQqoLnZg9C6dV5t6+vz6mRXl68QAN4ttLV1dk3MU1MDV1/t5/3d78J993lR5YorplZtjYsx4BNdPvaYB8vFi32tySVLvL1E5pTGRl9raN06/+Wwa9esnMJY3VkLUHdWERERqaSREZ9fo7vbJ+fJXVIkfzmRTCa9E888+STcc4+Hva1b4YUv9G6ulTQ46BP7mPm6k2vWwKJFsyt4i5RsfNz/ipLCiqS6s4qIiIhUUUMDLF3q2/btHiJzlxOJJ52JJ3XMZpPHtrWlp1CxbZt3N/3GN+Cpp7xr60tfWtl115ubfQvBg/eDD3pQXbnSQ+WCBelpD5Epy2RSGSDPR5XIAlSJFBERkWoaG/OA2dvrQ6f6+tK1RuWxY/DVr3oBZdkyuOGG6R3LODHhIXtkxMNlS4uPQ12yxNukkiFWRJxmZy2TQqSIiIikSV+frxCwf7/3fmtu9iBVzRlge3vhjjv83Nrb4RWvmLlZVkdHPVSOj3uoXLgQOjq822tra+W72IrMRwqRZVKIFBERkTSamPDJejo7fSbY2lrv7lqtCWgGBz1I9vR4sH35y32G1ZkUgldEBwe9fWprvUK5YoWH2tbWubHcishMU4gsk0KkiIiIpN3QkHcr3bvXQ1TMzINUXZ2Hy7q66Q1Ro6Nw551w6JC/1vXX+9qP1ZLNeqAcHvaAWVsLy5cn4ymbmhQqRUqhEFkmhUgRERGZTcbHk8l6RkZ8dtO+Pt8PDnqYMvM9JP82S0JnTY1vTU3ldwcdH4dvfQt27/bH7twJmzdX+l1OzsTEmaGyocGrlB0d3g1WgVKkMM3OKiIiIjKHZTK+tbScfV8IHizHxjxQZbO+j7exMd/ilQYOH/YuqeVM4JPJwHXXeZfWH/8Y7rrLg9ull1buPU5W3OW3rc2/Hh/35fk6O32W1w0bvErZ3Fzd8xSZTRQiRUREROYwMw9LpS6LsWwZPPKIT1JTzlhLM7jySg+y3/kOPPCAB9JLLoFVq9JT8ctkvAIJHp6ffBIef9zHUa5b5/s0zIArkmb6ERERERGRZ6xa5WMbf/CD8tdkNIPLLvOq3re/7dW+zk4PpBdfDFu3pms5jrq6ZCKggQH44Q+9crl6ta9L2d6envArkiYaE1mAxkSKiIjIfHfyJHz/+x4iC3WTPZ+hIa/wPfaYBzTwyua2bR4o29sre76VMjHh40lHR3186Nq1sHSpZnmV+UcT65RJIVJEREQETp/2IBmPK5yMiQmfQfbRR+HIEb/NDNas8a6uq1enN5yNjnqgnJjwIL12rVcuW1urfWYi008hskwKkSIiIiJucNCD5MTE1KuH3d3wk5/A00/784E/57ZtsGVLusPZyIgHymzWA/WaNV6h1IQ8MlcpRJZJIVJEREQkMTzsYyQHBnzimakaGoInnvDqZNzV1czHIW7bBuvXp2vsZL7hYT/viQkfN7p+vQfKciYiEkk7hcgyKUSKiIiInGlsDB56yMdKxpPRTFU2CwcOwK5dsG9fUp2sq4ONG30ino6O9HZ3BQ+UfX1JF91VqzxYpvmcRUqhEFkmhUgRERGRs42P+zqQXV2+FEglg9LwMOzZA089BUePJre3tXmY3LTJl+ZIazjLZn0M6eioh8iNGz1sp7miKnIuCpFlUogUERERKSyb9a6o+/Z5F86amsq/Rm+vh8ldu6C/P7k9Hou4Zo13fU1r99HhYQ+UtbV+rqtXT35iIpFqUYgsk0KkiIiISHEh+OQ4Tz3l1bbMNK08HgIcOuSvtX+/j6WM1dTAihVJSFuyJH1VyokJD5NjY16dXLjQw2RTEzQ0+FZfn77zFgGFyLIpRIqIiIicX2enz7a6ZMn0d9sMwWd3PXDAt2PHvCoaa272MLlqlVcp0zbT6/Cwd3UdHfX3EjPzc21t9YDZ0uIhs6lJXWGluhQiy6QQKSIiIlKaI0d8wp0FC6CxceZed2TEq5RxqIxneY0tWOCT8nR0eKhMa3fSEDxYjo35Nj6e3NfQ4NXLRYs8ZDY1eRvX1lbvfGX+UIgsk0KkiIiISOlOnPC1JJubq7NuYgg+a+zBg3D4sAfbkZEzj2lr8zDZ0eHVyrRVKgsZH/cK5shIMnOtmVcrFyzwfWurd4mNu8dOV9dimX8UIsukECkiIiJSntOn4Xvf8y6Y1Q5o2awH28OHC4fKeDmOCy+EtWunZ3Kg6RKCVyzj6mVu5RI8RLa2esBsa0vGYap6mU7Dw34d01gpV4gsk0KkiIiISPkGBuDBBz3YLFxY7bNJxKGyq8u7wB48mFT22to8TG7bVp0qaqVNTCRdY0dG/L3X1PgESCtWeAWztVWT+VRLNuvrivb0+B84Tp/2a3LNNdU+s7MpRJZJIVJERERkcoaH4Qc/gMFBWLy42mdT2PCwL1Py+OP+IR68Urd+PVx8sYetuRSyslmf2XZoyCuZmYyv87l8uQeYhgY/ZmIi2XK/jv9dV+fHNjb6v+dSG02neMmXI0d8m5jwa9Dc7PuhIdi5s9pnebZzhUj1mhYRERGRimlshCuugEcegaNHob19ZifcKUVjI/zUT8Fll3lV8tFHfQmR3bt9W7wYLroItmxJ71qU5aip8e6tLS3+9cRE0t03hPLDYBxEW1r8+sbLljQ2JsuWzCfZbFL9zd0GB30W4dOnvY0bG71Cn9u1OL878myhSmQBqkSKiIiITM3EhH+A3r3bu+81NnrYSGv1qr/fK5NPPOEf/sE/7G/YAFu3+mQ8s2ns5HTLZpMlS3KXLQnBg+SiRWfOKtvUlM72C8HfS1xtLVaRHR8/cxbd0dGkujs+7t/X+Uu3ZDL+fX+uP6KMj6sSKSIiIiICeADr6PCuob29sG+fd+WrrfXqVdpmEW1thec+F579bNi71wPl4cPw9NO+tbTA5s0eKNPaTXcm1dQUD0jj4159O37cQ1gIft1bWz1YLlzo1cqaGt/Mkn/nbvEfHEI4M6QW2goFv9wJiEZH/bzGxvy+0dEkHBYKgPl1ttxzrK31fSbj3YHn46RFKfvxFREREZG5xCypSg0N+cQ2e/f6h/eZXluyFLW1HhY3b/YK6q5d8NRTcOqUd9F95BGfpGbbNti0yStscqZ4htjcWXpD8Il+urq863B8W75SAl2h+wodlxv48rfm5uTfUj51Zy1A3VlFREREps/4uFep4q6u9fUeKNP6gT4EH9+5a5efc7xcSE2NLxWyffvsWypE0kHdWUVERERESpDJJF1dT53yyW0OHvT72trSV50083NdsQKuvBI6Oz1QHjjg/+7s9O6u27f7Vu11MkWmm0KkiIiIiFSFmY+PW7jQxxp2d3tX12PHPGi2t6dvvFkm491YN23yCXh27fLxk6dO+dImP/yhVycvvFDVSZm7FCJFREREpOrq62HlSt/6+nxSm85On/wkd3mKNGlu9mVCLr3Ux/o9/riH4P37fWtp8bGT27d7hVVkrlCIFBEREZFUaWtLJq45ccJndj12zCuXTU0eztK0VIhZEoCHh30inscf91lpf/hDeOghWL4cli3zSXkuuMCrrKpSymylECkiIiIiqZTJePBatswnH+nt9Qpld7cv41Bf7+MP07RcSGOjVyaf9Sxf0uTxx2HPHv/3kSPJcZkMLFmShMoLLvAZbNPWfVekkBT9yImIiIiIFBYvWN/R4TNanjrl1cmuLp8ttabGA2VaJuUx83Pt6ICrrvJz7e5Otr4+n/H16NHkMbW1HiaXL/dJfJYv9y6zImmjECkiIiIis0pcxVuyxMcb9vd7t9eDBz2sgQey5mYPldXu+trY6JPsrF2b3DY8fGao7O72YBwHyx/9yI9rbz8zVC5aVP33I6IQKSIiIiKzlpmPoWxrg3XrYHTUQ2Vvb1L9C8ErlY2NySLz1dbYCKtX+xYbGfFzPnrUu74eO+bB8tQpH2cJ0NAAS5d6uGxt9fcd75uaFDBlZihEioiIiMicUV8Pixf7tnGjz+7a3w+nT8Px48l4yngJkTQEylhDgy8PsmaNf53NeoU1Hk955AgMDJy5rmauTCYJlG1tXqldv15dYqXyFCJFREREZM6qrfWw2N7u4Syb9VB58KAvwxGvVZmmyXliNTXJpDuXXOK39fcnYyr7+vzr+N8jI16B7e1NnuPee31c5oYNvqVxqRSZfVL44yIiIiIiMj1qamDBArjoIl9CpKsLnn7au8G2t6dnYp5iWlt9K2R0NAmUfX1w6JCH5cOHfbvvPh9buXGjB8pizyNyPgqRIiIiIjIvNTR4d881a3z84e7dvm9unp0Bq74+mXAIfJmRkRGvuO7ZAwcOJN1i77/fJ+qJu7uaJeMpzTxs534ddxNuaKjKW5OUUYgUERERkXmttta7fK5YASdPeuA6dgzq6rxqOZvXbmxogC1bfBsd9UC5d6/v85cYKcWCBcn6lvE+DqEyfyhEioiIiIjgQSielKe/3yt3Bw74OMrWVp/9dDarr4fNm30bG/P3dvCgr7sZgm+Q/DubTb4eHoaeHp+g6PRpD6KxpqZk7OaKFbByZTrHmErl6PKKiIiIiORpbYULL/TA1d3toWmuVCfB38fGjb6VKpv1SXu6uz1QxvuhoSRwgwfIVau8m/C6dbOza7Ccm0KkiIiIiEgRdXXe1bWjI5msZv9+XzokXptxvqipSSq1sRC8XXp6fAmVgwd939np2733erfXOFAuW5auZVVkchQiRURERERK0NYG27cn1cl47GQm49XJ+diF08zf+4IFPuPrFVf4WpYHDnjYPnjQA2ZPDzz8sI/RXL3al1WJZ5qNt/nYfrOVLpWIiIiISBkyGR/7t2KFV+EOH/aq28SEr8M439dibGnxsL19u4+3PHLEA+X+/XDqlM+CW0hT09nBMp4pt6XF/z3buxHPFQqRIiIiIiKT1NYG27b5mpMnTiRjJ2trfd3J+V5dy2S88rh6NVx1lY+p7OryiYvyt6Eh344fL/58TU1JUI/DZe6miubMSE0Tm9lq4L3ADcASoAv4InBbCOHkJJ/zjcCnoi/fHEL4eCXOVUREREQkVybj4/2WLfPunF1dsG+fz4La3OwBR8tgeDfWhQvPvj2b9QCZHywHBmBw0P89OJgEze7u4q/R2JgEzLii2dLiAbSpya9HQ4PGZk5FKkKkmW0C7geWAV8CngCuAH4buMHMrg4h9JT5nGuADwH9gOaEEhEREZEZ0dLi4yY3bPB1Jzs7vTpp5ststLSoWpavpiapJi5fXviYbNaDZBwsBwaSoBnvBwZ8OZLh4XMHTTMPm83Nvs8Nl3V1vmUyvsVfx7fV1fl1nM8hNC3fvh/GA+TbQggfim80sw8AvwP8CXBLqU9mZgZ8EugB/hN4Z0XPVkRERETkPGprk/UTh4d9fcXubjh6FEZG/JiGBg8vCpXnV1OTVBaLCaF4wIyrmENDfj3if09Wfb1fv2JbXd25zxN8HO3EBOzcOfnzqIaqf7ua2UbgemAf8Hd5d78HeAvwRjN7RwhhoMSnfRvwYmBntBcRERERqZrGRt+WLYOLLvKg09fn4/+OHYPR0eQ4TSAzeWZJRXPZsuLHTUycGSSHhvyajI56F+Tx8TP3+f8eHU22vr7Jn282e+6wmVZVD5EkIe/OEEI2944QQp+Z3YeHzOcDd53vyczsQuB9wF+HEO4xM4VIEREREUmV5mbfli/3qtTQkIeRo0d9NtOJiaTy1thY7bOde2prpzaTbggeIEdGim9jY2c+ptCY2ImJpCo5m6QhRG6L9k8VuX8XHiK3cp4QaWYZ4NPAfuD3K3WCIiIiIiLTxezMUHnJJR4oe3p8gp7jxz1oxOP2VKWsPrOk2+pUjI9PrUtttaQhRLZH+1NF7o9vLzCP01n+ELgcuCaEUNblMLO34F1nWbt2bTkPFRERERGpmJoaXx6kvR02bkzGUx454ls2mxxXX3/mJDAiM2E2fKvFhd9zFnrN7Aq8+viXIYQHyn2REMLHgI8B7NixYxYWlUVERERkLsodTxlXKePxfPGMpAMDyWQ9MTMPorNxzJ2kWxpCZFxpbC9y/4K8486S0431KeDdlTs1EREREZH0yK1S5stmz57wZd8+X2aksRHa2rRWpVRGGkLkk9F+a5H7t0T7YmMmwdeBjB8/bIV/Ov7BzP4Bn3Dn7WWfpYiIiIhIitXUJFVL8KVF1q2D3l44cMDHV4KHSU3WI1ORhhB5d7S/3sxqcmdoNbM24GpgCPjOOZ5jBPhEkfuejY+TvBcPrGV3dRURERERmY1qamDxYt+2b/dJevbs8WVF6us9UGqiHilX1UNkCGG3md2Jz8D6VuBDOXffBrQAH43XiDSzOmATMBZC2B09xxBwc6HnN7Nb8RD5TyGEj0/X+xARERERSbOGBli9Glat8ol6Dh2CgweTiXrAu7vW1HiwLLSJQApCZOQ3gPuBvzGz64DHgecB1+LdWP93zrGrovs7gfUze5oiIiIiIrNbPOFOezts2QKDg77UxMSEr204MnLmGohjYz6Jz+ioLzVSV+dLjUx1eQuZvVIRIqNq5A7gvcANwCuALuBvgNtCCCeqeX4iIiIiInNRXV3hSXoKGR6G/n44ccK7xR4/7rfX1HiobGzUxD3zhYWg1Szy7dixIzz44IPVPg0RERERkdQaHfVQ2dvrYyxPnfKusU1N0NqqQFmK8XGv8u7cWe0zOZuZ/SCEsKPQfamoRIqIiIiIyOxSX59M2rNxowei06ehsxOOHPExlAsWaJ3KuUghUkREREREpiyTSULl0JAvKbJ3r1csm5u9Oilzg0KkiIiIiIhUVFOTVyfXr/cxlPv2eZfX2lofg5lRCpnVdPlERERERGRa1NTABRf4NjCQVCcnJnzMZH29T8hTX1/tM5VyKESKiIiIiMi0a2mBzZthwwafkGdgAE6e9ErlqVPJcXGw1BIi6aUQKSIiIiIiMybu0treDitX+m3j475e5eCgB8uenmQJETMPlU1N/lipPoVIERERERGpqkzGZ3JdsABWrPDbxse9Wnn6tAfKnh7vBgterWxu1syv1aIQKSIiIiIiqZPJJBXLNWsgBK9U9vdDd7dP1NPb67c3NHh3WU3YMzPUzCIiIiIiknpmHhRbWmD5crj4Yhgehr4+r1R2dflyIrW13vW1qckfI5WnECkiIiIiIrNSY6NvS5fChRcmk/V0dXm1MgTv+trSoq6vlaQQKSIiIiIis54ZtLb6tmYNjI0l4ym7ujxcmnmX16Ym7wKrSuXkKESKiIiIiMicU1cHS5b4tm2bd30dGPAlRY4f90olaPbXyVCIFBERERGROc0sGSd5wQWwdeu5Z38FD6H19b5pwp4zqTlERERERGTeKTT769CQVyxHRnzCntOnfT8ykjzOzINlU9P8HWepECkiIiIiIvOema892dzsX3d0JPeNj3uQHBnxoNnbmywxAvNvnKVCpIiIiIiIyDlkMr61tPjXq1YlS4zEM8IeO+ZdYkM4s/tsTU11z306KESKiIiIiIhMQrzEyJIlsHmzVyz7+5O1K7u7fZxlJuOzxtbXV/uMK0MhUkREREREpAIyGVi40Lc1azxAnj7tFcrDh737a1yljLvNzkYKkSIiIiIiItOgthYWLfJt8+ZkPGVXl1cpx8eTLrKziUKkiIiIiIjIDIjHSXZ0JFXK8fFqn1X5FCJFRERERERmWFylnI3m4FxBIiIiIiIiMl0UIkVERERERKRkCpEiIiIiIiJSMoVIERERERERKZlCpIiIiIiIiJRMIVJERERERERKphApIiIiIiIiJVOIFBERERERkZIpRIqIiIiIiEjJFCJFRERERESkZAqRIiIiIiIiUjKFSBERERERESmZQqSIiIiIiIiUTCFSRERERERESqYQKSIiIiIiIiVTiBQREREREZGSKUSKiIiIiIhIyRQiRUREREREpGQKkSIiIiIiIlIyhUgREREREREpmYUQqn0OqWNmx4HOap9HARcA3dU+iXlM7V99ugbVpfavLrV/9ekaVJfav7rU/tU309dgXQhhaaE7FCJnETN7MISwo9rnMV+p/atP16C61P7VpfavPl2D6lL7V5fav/rSdA3UnVVERERERERKphApIiIiIiIiJVOInF0+Vu0TmOfU/tWna1Bdav/qUvtXn65Bdan9q0vtX32puQYaEykiIiIiIiIlUyVSRERERERESqYQKSIiIiIiIiVTiEw5M1ttZv9oZofNbMTM9pnZB813s4Y3AAATjklEQVRsUbXPba4ws58zsw+Z2X+b2WkzC2b2mfM85iozu8PMTpjZoJn9yMzebma1M3Xec4GZLTGzm83sC2b2tJkNmdkpM7vXzH7NzAr+jlL7V5aZvd/M7jKzA9E1OGFmD5nZe8xsSZHH6BpMEzN7Y/R7KJjZzUWOUftXSPT/aiiyHSnyGLX/NDCzF5jZ582sK/rM02Vmd5rZKwocq2tQAWZ20zm+/+NtosDj1P4VZGavjL7XD0b/D+8xs8+a2ZVFjq96+2tMZIqZ2SbgfmAZ8CXgCeAK4FrgSeDqEEJP9c5wbjCzh4HLgH7gILAd+OcQwhuKHP8zwOeBYeDfgRPAjcA24HMhhNfNxHnPBWZ2C/D3QBdwN7AfWA68FmjH2/l1IecXldq/8sxsFPgh8BhwDGgBng/sAA4Dzw8hHMg5XtdgmpjZGuDHQC3QCrw5hPDxvGPU/hVkZvuAhcAHC9zdH0L4i7zj1f7TwMz+APgjfCH1r+D/L1wAXA7cHUJ4V86xugYVYmY/Bby6yN0vAF4M/FcI4adzHqP2ryAzez/wLqAH+CL+M7AZeBWQAX45hPCZnOPT0f4hBG0p3YCvAwH4rbzbPxDd/pFqn+Nc2PBQvgUwYGfUtp8pcuwC/EP2CLAj5/ZGPPAH4Beq/Z5my4b/53QjUJN3+wo8UAbgZ9X+034dGovc/idRm35Y12BGroMB/w/YDfx51JY35x2j9q98u+8D9pV4rNp/eq7B66K2+wbQVuD+Ol2DqlyXB6L2fJXaf9raeAUwARwBluXdd23UnnvS2P7qzppSZrYRuB7/z+3v8u5+DzAAvNHMWmb41OacEMLdIYRdIfopPI+fA5YC/xZCeDDnOYaBP4i+/J/TcJpzUgjhmyGEL4cQsnm3HwE+En25M+cutf80iNqvkP+I9ltybtM1mD5vw/+w8ib8d3whav/qUvtXWDRs4f3AIPBLIYS+/GNCCGM5X+oazAAzuwTvkXII+K+cu9T+lbUOH1743RDCsdw7Qgh3A314e8dS0/4Kken14mh/Z4EP2H3AfUAz/gMuMye+Ll8rcN89+H+CV5lZw8yd0pwVf2gYz7lN7T+zboz2P8q5TddgGpjZhcD7gL8OIdxzjkPV/tOjwczeYGa/b2a/bWbXFhlbpPavvKuADcAdwMlobNjvRteh0HgwXYOZ8evR/hMhhNwxkWr/ytoFjAJXmNkFuXeY2QuBNryHSiw17Z+Z7heQSdsW7Z8qcv8uvFK5FbhrRs5I4BzXJYQwbmZ7gYuBjcDjM3lic4mZZYBfjr7M/UWp9p9GZvZOfBxeOz4e8ho8QL4v5zBdgwqLvt8/jXfh/v3zHK72nx4r8GuQa6+ZvSmE8O2c29T+lffcaH8UH5v9rNw7zewe4OdCCMejm3QNppmZNQFvALLAx/PuVvtXUAjhhJn9Lj5U7TEz+yI+NnITPibyGySBHlLU/gqR6dUe7U8VuT++feEMnIskdF1mxvuAS4A7Qghfz7ld7T+93olPbBT7GnBTzoc30DWYDn+ITx5yTQhh6DzHqv0r75PAfwOP4l3HNgK/CbwF+KqZXRlCeCQ6Vu1fecui/S3AXuAlwHfxbn5/CbwM+CzJ0AZdg+n3erz9/ivkTKoWUftXWAjhg9EEX/8IvDnnrqeB2/O6uaam/dWddfayaK/pddNF12WKzOxtwDvw2YjfWO7Do73afxJCCCtCCIZXZV6Lf5h+yMyeXcbT6BqUwcyuwKuPfxlCeKASTxnt1f4lCiHcFo3PPhpCGAwh/CSEcAteGWgCbi3j6dT+5Yu7DRtecbwrhNAfQngUeA0+a/qLii11UICuwdS9Jdp/dBKPVfuXyczeBXwOuB2vQLYAzwH2AP9sZn9WztNF+2lvf4XI9Ir/ktBe5P4FecfJzNB1mUZm9lbgr/GlJq4NIZzIO0TtPwOiD9NfwLvMLwE+lXO3rkGF5HRjfQp4d4kPU/vPnHhyrxfm3Kb2r7yT0X5PTsUXgKgyH/dGuSLa6xpMIzO7CB+nehAfp5pP7V9BZrYTn1jq/4YQ/lcIYU/0x6wf4n9EOQS8I5pwE1LU/gqR6fVktN9a5P54tsRiYyZlehS9LtEHwg34RDB7ZvKk5gIzezvwt8BP8ABZaJFvtf8MCiF04oH+4pwB/7oGldOKt+OFwHDu4t74LNwA/xDdFq9hqPafOXEXstxZ0NX+lRe3aW+R++OQ2ZR3vK7B9Cg2oU5M7V9Z8fqbd+ffEUIYBL6H57XLo5tT0/4KkekVfzNdH01//QwzawOuBoaA78z0ic1z34z2NxS474X4jLn3hxBGZu6UZr9oUPlfAQ/jAfJYkUPV/jNvZbSPP0zoGlTOCPCJIttD0TH3Rl/HXV3V/jMn7j6Z+2FM7V959+AfereYWX2B+y+J9vuiva7BNDGzRnwYSRb/vVOI2r+y4llUlxa5P759NNqnp/1nYjFKbZPb8C4cAfitvNs/EN3+kWqf41zb8IH7AfhMkfsXAMdJwSKvc2XDu/EF4EFg8XmOVftXvv23AysK3F4D/EnUpvfpGsz4dbk1asub825X+1e2nS8u9HsHn9RlV9Sev6/2n/br8Jmo7f447/aX4oGmF1ioazDt1+GNUft9+RzHqP0r2+avj9rsCLAq776XR9//Q8CStLW/RS8sKWRmm/BviGXAl/Cpep8HXIt3Y70qhNBTvTOcG8zs1cCroy9X4DPB7cFn6wPoDiG8M+/4zwHDwL8BJ/BpmLdFt78+6AerJGb2K/hA8gngQxTuw78vhHB7zmPU/hUUdSP+c7wasBufWnw58CJ8Yp0jwHUhhMdyHqNrMM3M7Fa8S+ubQwgfz7tP7V8hUTv/Ht77Zy8+O+sm4JX4h7I7gNeEEEZzHqP2rzAzW4avf70Z/7/3e3iQfw3+ofiXQgifzTle12AamNl/40s7vSqE8OVzHKf2r5Cot+HX8VmJ+4Av4P/vXoh3dTXg7SGEv855TDrav9oJXNu5N2ANPv14F17K7sQnHjlnxUZbWW18K/6fVLFtX4HHXE20MDL+F6IfA78D1Fb7/cymrYS2D8C31P7Teg0uAf4O70rcjXcrOwV8P7o+BX/X6BpM+3WJfzZuLnK/2r8y7fwi4F/x2aB7gTH8r/zfwNeqNbX/jF2LxXhPq73R550e/A/oz9c1mJH2vzD6nXOglDZU+1e07euAt+ND1E5H/w8fA74CXJ/W9lclUkREREREREqmiXVERERERESkZAqRIiIiIiIiUjKFSBERERERESmZQqSIiIiIiIiUTCFSRERERERESqYQKSIiIiIiIiVTiBQREREREZGSKUSKiMi8YGY3mVkws5um+Dy3Rs+zszJnJiIiMrsoRIqISNnMrNbM3mxm3zazE2Y2ZmbHzOxHZvZxM3tVFc5pZxTubp3p1z6XnNBZbNtX7XMUEREpR6baJyAiIrOLmdUCXwFuAHqB/wIOAouBTcAvAduB/1utcyziC8B3gK4qvf63gW8VuL13hs9DRERkShQiRUSkXL+IB8hHgBeFEE7l3mlmzcDzqnFi5xKd56nzHjh9vhVCuLWKry8iIlIR6s4qIiLluira354fIAFCCIMhhLtzb8sdj2hmrzSz+81swMxOmtnnzGxL/vOY2VYze5+ZPWhmx81sxMw6zexjZrY679jbgfg135PXXXRn/jnkPfba6DkfM7PTZjZkZj8xs/eYWeNkG2kyzGxzdI4fN7NtZvbZ6L1nzeyanOOWmNn7zeyJ6Hx7zewbZvaSIs+7wMw+aGYHzWzYzB43s7eb2Zb49fKOv9fMxos8183RY95Q4L41ZvZhM9sTXa8eM/uSmT2nwLF/HD3PNWb282b2fTMbjB7zL2bWUeT1l5jZn5rZo9Hxp8zs4ei2puiY75vZhJmtKfIcvxe99m8Xul9ERM5NlUgRESlXT7TfOonHvhZ4Od619FvATwE/C1xrZleFEJ7MO/YWPBzeD4wCFwM3Azea2Y4QwqHo2C9G+1/h7G6j+85zTr+Ld7+9H++a2whcDdwK7DSzl4QQJsp8n1O1Ffge8BjwGaAZ6AMwsw14m6wD7gHuANqAnwbuNLNfCyF8Mn6iKAh/E3gO8DDwz8Ai/P1dW6kTNrMdwNej5/4a8HlgKfAa4AYzuzGEcGeBh74NuBHv/vwt4Eq82n2ZmV0eQhjNeY1N0XtfAzwIfBioBbYB74i+Pgj8PfAJ/HvlPXnnadHtw8CnKvDWRUTmHYVIEREp13/iwesWM2vDA+EPQgidJTz2RuDGEMJX4huiatAH8QBwXc6xnwb+KoQwkvsEZnY98FXgD4D/CRBC+KKZ9eIhstxuo78B7A0hhLzX+aPoNX4O+Pcynq+YnUUm/bk9hLAv77YXAH8UQvjDAsd/GlgLvD6E8Nmc812Eh8q/NbOvhBCOR3e9Cw+Q/wH8YgghGx3/fuAHU3g/zzCzuuj5m4EXhhDuzbnvD4DvA/9oZhtzQ2HkZcCOEMKj0fGGt/fr8GD8nznH/gseIN8VQvjzvHNYCpyOvvxX4C+AXzOz9+b9EeA6fOzup0IIJ6fwtkVE5i11ZxURkbKEEB4C3gAcjfafB/ZF3RC/YGY3nuPh38wNkJG/BXYDLzazdTmvcyg/QEa33wk8ioePKQsh7MkPkJEPRvuKvA7wIrwqlr+tL3DsYeCP82+MuoVeDfx7boAEiALRrXiQe03OXW8CJoDfiwNkdPxuvO0r4VXABuCDuQEyep2DeKBbBews8Ni/igNkdHwA/iH68or4djN7XvT1g9HznSGEcDz+fgkhDAH/FL3mT+cd+uvR/qMlvjcREcmjSqSIiJQthPAfZvYFvDvkNcDl0f7VwKvN7FPATQXC2bcLPNeEmd2LV4cuBzrhmYrU/wBuAi7Du0nW5jw0v6I1KWbWAvw2Hry24l1DLeeQVZV4HeC2MiqkDxeo2IF39QRYVKSquTzaXwjPVCfX45XWvQWO/xbwv0s8p3OJz2tDkfPalnNe+V1aHyxw/IFovyjntudH+68XCf35/h6/rr8OfAnAzJYBPwP8OIRwfwnPISIiBShEiojIpIQQxvBAcCc8s/THzwL/CPwy3s31i3kPO1rk6Y5E+/ac2z4AvB1fkuPrwCFgKLrvJnxM4JRE3TC/iVe4foJ3ozwOjEWHvAdomOrrTMKRIrcvifYv49wV0tZoH7fn+dp9quLz+vnzHNda4LZCS5zEk/rk/tFgYbQ/RAlCCE+Z2V3Ay8xsXdTd+leBOlSFFBGZEoVIERGpiGjc2X+Y2bPwsYQv5uwQufysB7oV0f4UPFMxehse7K4KIfTlHmxmv1ih0/4ZPED+UwjhprzX6CBvUpYZVKzSFs+G+9YQwodLeJ74+PO1e74sXgyuye0CG1lY4Pj4dV4ZQrijhPOajDhsllMZ/nvgJcCbzezd+IQ6g/hkRSIiMkkaEykiIpUWBz4rcN+L8m+IKpjx8hUPRfuN+P9RdxYIkKuj+/PFk6fUFrivmM3R/vOlnGsKfCfav6CUg6NxkvuAtWa2vsAhO4s89CTe/oUC246pntckxa/xsqircym+hFcufxVf23QT8G+FlqYREZHSKUSKiEhZzOwXzeylZnbW/yFmtgJ4c/TlPQUe/mIzy5/o5DfxD/d358zwui/aXxOFzPj5W/FJVwr1pImXHllb0hs583V25t5oZhuB95fxPDMihPAd4AHg9Wb2K4WOMbPLzOyCnJs+iQfr9+des2i5jN8s8lLfi/Zvzr0xmhn3dQWO/wLelm8zs4LdbM3sKpvCupshhO9G57UDeGeB57/AzBryHjOBf790kEzW85HJnoOIiDh1ZxURkXI9D5+w5Eg0IU48YcsG4JVAE14B+lyBx34Z+EI0Kc/T+IQ5rwBO4EttABBCOGJm/wb8AvCwmd2Jj+97Kb6+38P4GpO5nsSrTr9gZqPAfrxb6KfPsfzIl6Pz+F9RN9yH8BD60/iakeUE0pnyC8BdwO1m9nY8WPUCq/E2uQh4LtAdHf/neLfd1wNborZcHH39bXxm1XyfwNddfLeZXQ48jq+leQMeGH829+AQwoiZvRZfH/JrZnYffo2G8DZ8Lv79sRS/fpP1S/hkQH9mZq+Lzr8G2AJcj1eWD+Y95mN49+pVwEMhhO9P4fVFRARVIkVEpHx/iVewvgNcCtyCT4BzDf4B/43Aa4vMoPmf+Cyoa/AgenV025UhhCfyjv014E/xUPpWfCKZrwBXkYzBe0ZUdXoNcC8ekG4D/ggPLwWFEAbwsZv/AlyMj8O8NHrcG87VCNUSQtiPr/v4bjwk/w/gt/AZUvcCbwEeyzl+CH+Pf4OPgfwd4IV4+5xV0YsecwTvzvs1vEr7G/istddFtxV6zEN42/0ZPqvqr+LreD4bX4/yDXg32UmLliV5Nr7Ex0L8ff8q/v30FyTV6NzHdOWcsybUERGpACttlmwREZHJM7Ob8G6Vbwoh3F7ds5GYmW0GdgGfCCHcXO3zmQ5Rd+g9ePW1I4TQX+VTEhGZ9VSJFBERkbns5/EutbcrQIqIVIbGRIqIiMicEs3e+rv4+pVvAfpJ4URJIiKzlUKkiIiIzDW1wP8BxoBHgXeGEPIn3BERkUnSmEgREREREREpmcZEioiIiIiISMkUIkVERERERKRkCpEiIiIiIiJSMoVIERERERERKZlCpIiIiIiIiJRMIVJERERERERK9v8BoHvzC4jkmKgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = int(X.shape[0]/2)\n",
    "num_feat = X.shape[1]\n",
    "\n",
    "psd1D_org_0 = np.zeros((num,num_feat))\n",
    "psd1D_org_1 = np.zeros((num,num_feat))\n",
    "psd1D_org_0_mean = np.zeros(num_feat)\n",
    "psd1D_org_0_std = np.zeros(num_feat)\n",
    "psd1D_org_1_mean = np.zeros(num_feat)\n",
    "psd1D_org_1_std = np.zeros(num_feat)\n",
    "\n",
    "cont_0=0\n",
    "cont_1=0\n",
    "\n",
    "# We separate real and fake using the label\n",
    "for x in range(X.shape[0]):\n",
    "    if y[x]==0:\n",
    "        psd1D_org_0[cont_0,:] = X[x,:]\n",
    "        cont_0+=1\n",
    "    elif y[x]==1:\n",
    "        psd1D_org_1[cont_1,:] = X[x,:]\n",
    "        cont_1+=1\n",
    "\n",
    "# We compute statistcis\n",
    "for x in range(num_feat):\n",
    "    psd1D_org_0_mean[x] = np.mean(psd1D_org_0[:,x])\n",
    "    psd1D_org_0_std[x]= np.std(psd1D_org_0[:,x])\n",
    "    psd1D_org_1_mean[x] = np.mean(psd1D_org_1[:,x])\n",
    "    psd1D_org_1_std[x]= np.std(psd1D_org_1[:,x])\n",
    "    \n",
    "# Plot\n",
    "x = np.arange(0, num_feat, 1)\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "ax.plot(x, psd1D_org_0_mean, alpha=0.5, color='red', label='Fake', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_0_mean - psd1D_org_0_std, psd1D_org_0_mean + psd1D_org_0_std, color='red', alpha=0.2)\n",
    "ax.plot(x, psd1D_org_1_mean, alpha=0.5, color='blue', label='Real', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_1_mean - psd1D_org_1_std, psd1D_org_1_mean + psd1D_org_1_std, color='blue', alpha=0.2)\n",
    "\n",
    "plt.tick_params(axis='x', labelsize=20)\n",
    "plt.tick_params(axis='y', labelsize=20)\n",
    "ax.legend(loc='best', prop={'size': 20})\n",
    "plt.xlabel(\"Spatial Frequency\", fontsize=20)\n",
    "plt.ylabel(\"Power Spectrum\", fontsize=20)\n",
    "#plt.savefig('1000_celeba.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we classify using the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average SVM: 0.9995\n",
      "Average SVM_r: 1.0\n",
      "Average SVM_p: 0.9855\n",
      "Average LR: 0.9975000000000002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "num = 10\n",
    "LR = 0\n",
    "SVM = 0\n",
    "SVM_r = 0\n",
    "SVM_p = 0\n",
    "\n",
    "\n",
    "for z in range(num):\n",
    "    # read python dict back from the file\n",
    "    pkl_file = open('celeba_low_1000.pkl', 'rb')\n",
    "    \n",
    "    data = pickle.load(pkl_file)\n",
    "\n",
    "    pkl_file.close()\n",
    "    X = data[\"data\"]\n",
    "    y = data[\"label\"]\n",
    "\n",
    "\n",
    "    try:\n",
    "\n",
    "        from sklearn.model_selection import train_test_split\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "        from sklearn.svm import SVC\n",
    "        svclassifier = SVC(kernel='linear')\n",
    "        svclassifier.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(svclassifier.score(X_test, y_test)))\n",
    "               \n",
    "        from sklearn.svm import SVC\n",
    "        svclassifier_r = SVC(C=6.37, kernel='rbf', gamma=0.86)\n",
    "        svclassifier_r.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(svclassifier_r.score(X_test, y_test)))\n",
    "                \n",
    "        from sklearn.svm import SVC\n",
    "        svclassifier_p = SVC(kernel='poly')\n",
    "        svclassifier_p.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(svclassifier_p.score(X_test, y_test)))\n",
    "        \n",
    "        from sklearn.linear_model import LogisticRegression\n",
    "        logreg = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "        logreg.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(logreg.score(X_test, y_test)))\n",
    "\n",
    "        \n",
    "        SVM+=svclassifier.score(X_test, y_test)\n",
    "        SVM_r+=svclassifier_r.score(X_test, y_test)\n",
    "        SVM_p+=svclassifier_p.score(X_test, y_test)\n",
    "        LR+=logreg.score(X_test, y_test)\n",
    "\n",
    "        \n",
    "    except:\n",
    "        num-=1\n",
    "        print(num)\n",
    "    \n",
    "print(\"Average SVM: \"+str(SVM/num))\n",
    "print(\"Average SVM_r: \"+str(SVM_r/num))\n",
    "print(\"Average SVM_p: \"+str(SVM_p/num))\n",
    "print(\"Average LR: \"+str(LR/num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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